diff --git a/src/TensorFlowNET.Console/MemoryMonitor.cs b/src/TensorFlowNET.Console/MemoryMonitor.cs index 92cd224f..f9a6bfd1 100644 --- a/src/TensorFlowNET.Console/MemoryMonitor.cs +++ b/src/TensorFlowNET.Console/MemoryMonitor.cs @@ -23,11 +23,9 @@ namespace Tensorflow var x = tf.placeholder(tf.float64, shape: (1024, 1024)); var log = tf.log(x); - using (var sess = tf.Session()) - { - var ones = np.ones((1024, 1024), dtype: np.float64); - var o = sess.run(log, new FeedItem(x, ones)); - } + var sess = tf.Session(); + var ones = np.ones((1024, 1024), dtype: np.float64); + var o = sess.run(log, new FeedItem(x, ones)); // Thread.Sleep(1); } diff --git a/src/TensorFlowNET.Core/Buffers/Buffer.cs b/src/TensorFlowNET.Core/Buffers/Buffer.cs index bb4b880a..9ec9e22f 100644 --- a/src/TensorFlowNET.Core/Buffers/Buffer.cs +++ b/src/TensorFlowNET.Core/Buffers/Buffer.cs @@ -25,15 +25,15 @@ namespace Tensorflow /// /// Represents a TF_Buffer that can be passed to Tensorflow. /// - public sealed class Buffer : IDisposable + public sealed class Buffer { - public SafeBufferHandle Handle { get; } + SafeBufferHandle _handle; /// /// /// private unsafe ref readonly TF_Buffer DangerousBuffer - => ref Unsafe.AsRef(Handle.DangerousGetHandle().ToPointer()); + => ref Unsafe.AsRef(_handle.DangerousGetHandle().ToPointer()); /// /// The memory block representing this buffer. @@ -59,7 +59,7 @@ namespace Tensorflow { get { - using (Handle.Lease()) + using (_handle.Lease()) { return DangerousBuffer.length; } @@ -67,13 +67,13 @@ namespace Tensorflow } public Buffer() - => Handle = TF_NewBuffer(); + => _handle = TF_NewBuffer(); public Buffer(SafeBufferHandle handle) - => Handle = handle; + => _handle = handle; public Buffer(byte[] data) - => Handle = _toBuffer(data); + => _handle = _toBuffer(data); private static SafeBufferHandle _toBuffer(byte[] data) { @@ -92,7 +92,7 @@ namespace Tensorflow /// public unsafe byte[] ToArray() { - using (Handle.Lease()) + using (_handle.Lease()) { ref readonly TF_Buffer buffer = ref DangerousBuffer; @@ -107,7 +107,12 @@ namespace Tensorflow } } - public void Dispose() - => Handle.Dispose(); + public override string ToString() + => $"0x{_handle.DangerousGetHandle():x16}"; + + public static implicit operator SafeBufferHandle(Buffer buffer) + { + return buffer._handle; + } } } \ No newline at end of file diff --git a/src/TensorFlowNET.Core/Checkpoint/CheckpointReader.cs b/src/TensorFlowNET.Core/Checkpoint/CheckpointReader.cs index ffefe312..a1dba371 100644 --- a/src/TensorFlowNET.Core/Checkpoint/CheckpointReader.cs +++ b/src/TensorFlowNET.Core/Checkpoint/CheckpointReader.cs @@ -11,7 +11,7 @@ public class CheckpointReader Status status = new Status(); VariableToDataTypeMap = new Dictionary(); VariableToShapeMap = new Dictionary(); - _handle = c_api.TF_NewCheckpointReader(filename, status.Handle); + _handle = c_api.TF_NewCheckpointReader(filename, status); status.Check(true); ReadAllShapeAndType(); } @@ -38,7 +38,7 @@ public class CheckpointReader int num_dims = GetVariableNumDims(name); long[] dims = new long[num_dims]; Status status = new Status(); - c_api.TF_CheckpointReaderGetVariableShape(_handle, name, dims, num_dims, status.Handle); + c_api.TF_CheckpointReaderGetVariableShape(_handle, name, dims, num_dims, status); status.Check(true); return new Shape(dims); } @@ -49,7 +49,7 @@ public class CheckpointReader public unsafe Tensor GetTensor(string name, TF_DataType dtype = TF_DataType.DtInvalid) { Status status = new Status(); - var tensor = c_api.TF_CheckpointReaderGetTensor(_handle, name, status.Handle); + var tensor = c_api.TF_CheckpointReaderGetTensor(_handle, name, status); status.Check(true); return new Tensor(tensor); } diff --git a/src/TensorFlowNET.Core/Contexts/Context.Device.cs b/src/TensorFlowNET.Core/Contexts/Context.Device.cs index fea2c824..97c550e8 100644 --- a/src/TensorFlowNET.Core/Contexts/Context.Device.cs +++ b/src/TensorFlowNET.Core/Contexts/Context.Device.cs @@ -37,7 +37,7 @@ namespace Tensorflow.Contexts public void log_device_placement(bool enable) { if (_handle != null) - c_api.TFE_ContextSetLogDevicePlacement(_handle, enable, tf.Status.Handle); + c_api.TFE_ContextSetLogDevicePlacement(_handle, enable, tf.Status); _log_device_placement = enable; // _thread_local_data.function_call_options = null; } @@ -60,15 +60,15 @@ namespace Tensorflow.Contexts public PhysicalDevice[] list_physical_devices(string device_type = null) { using var opts = c_api.TFE_NewContextOptions(); - using var ctx = c_api.TFE_NewContext(opts, tf.Status.Handle); - using var devices = c_api.TFE_ContextListDevices(ctx, tf.Status.Handle); + using var ctx = c_api.TFE_NewContext(opts, tf.Status); + using var devices = c_api.TFE_ContextListDevices(ctx, tf.Status); tf.Status.Check(true); int num_devices = c_api.TF_DeviceListCount(devices); var results = new List(); for (int i = 0; i < num_devices; ++i) { - var dev_type = c_api.StringPiece(c_api.TF_DeviceListType(devices, i, tf.Status.Handle)); + var dev_type = c_api.StringPiece(c_api.TF_DeviceListType(devices, i, tf.Status)); tf.Status.Check(true); if (dev_type.StartsWith("XLA")) @@ -76,7 +76,7 @@ namespace Tensorflow.Contexts if (device_type == null || dev_type == device_type) { - var dev_name = c_api.TF_DeviceListName(devices, i, tf.Status.Handle); + var dev_name = c_api.TF_DeviceListName(devices, i, tf.Status); tf.Status.Check(true); results.Add(new PhysicalDevice diff --git a/src/TensorFlowNET.Core/Contexts/Context.cs b/src/TensorFlowNET.Core/Contexts/Context.cs index 5d02c027..21a14831 100644 --- a/src/TensorFlowNET.Core/Contexts/Context.cs +++ b/src/TensorFlowNET.Core/Contexts/Context.cs @@ -28,7 +28,7 @@ namespace Tensorflow.Contexts /// /// Environment in which eager operations execute. /// - public sealed partial class Context : IDisposable + public sealed partial class Context { public const int GRAPH_MODE = 0; public const int EAGER_MODE = 1; @@ -41,15 +41,7 @@ namespace Tensorflow.Contexts public FunctionCallOptions FunctionCallOptions { get; } SafeContextHandle _handle; - public SafeContextHandle Handle - { - get - { - if (_handle == null) - ensure_initialized(); - return _handle; - } - } + int? _seed; Random _rng; @@ -59,6 +51,7 @@ namespace Tensorflow.Contexts context_switches = new ContextSwitchStack(defaultExecutionMode == EAGER_MODE, false); initialized = false; FunctionCallOptions = new FunctionCallOptions(); + ensure_initialized(); } /// @@ -72,12 +65,12 @@ namespace Tensorflow.Contexts Config = MergeConfig(); FunctionCallOptions.Config = Config; var config_str = Config.ToByteArray(); - using var opts = new ContextOptions(); - using var status = new Status(); - c_api.TFE_ContextOptionsSetConfig(opts.Handle, config_str, (ulong)config_str.Length, status.Handle); + var opts = new ContextOptions(); + var status = new Status(); + c_api.TFE_ContextOptionsSetConfig(opts, config_str, (ulong)config_str.Length, status); status.Check(true); - c_api.TFE_ContextOptionsSetDevicePlacementPolicy(opts.Handle, _device_policy); - _handle = c_api.TFE_NewContext(opts.Handle, status.Handle); + c_api.TFE_ContextOptionsSetDevicePlacementPolicy(opts, _device_policy); + _handle = c_api.TFE_NewContext(opts, status); status.Check(true); initialized = true; } @@ -178,10 +171,14 @@ namespace Tensorflow.Contexts tf.Context.ensure_initialized(); if (_handle != null) + { c_api.TFE_ContextClearCaches(_handle); + } } - public void Dispose() - => _handle.Dispose(); + public static implicit operator SafeContextHandle(Context ctx) + { + return ctx._handle; + } } } diff --git a/src/TensorFlowNET.Core/Contexts/ContextOptions.cs b/src/TensorFlowNET.Core/Contexts/ContextOptions.cs index 6c2156a9..4a07f1f5 100644 --- a/src/TensorFlowNET.Core/Contexts/ContextOptions.cs +++ b/src/TensorFlowNET.Core/Contexts/ContextOptions.cs @@ -14,21 +14,21 @@ limitations under the License. ******************************************************************************/ -using System; using Tensorflow.Eager; -namespace Tensorflow.Contexts +namespace Tensorflow.Contexts; + +public sealed class ContextOptions { - public sealed class ContextOptions : IDisposable - { - public SafeContextOptionsHandle Handle { get; } + SafeContextOptionsHandle _handle { get; } - public ContextOptions() - { - Handle = c_api.TFE_NewContextOptions(); - } + public ContextOptions() + { + _handle = c_api.TFE_NewContextOptions(); + } - public void Dispose() - => Handle.Dispose(); + public static implicit operator SafeContextOptionsHandle(ContextOptions opt) + { + return opt._handle; } } diff --git a/src/TensorFlowNET.Core/Eager/EagerRunner.TFE_Execute.cs b/src/TensorFlowNET.Core/Eager/EagerRunner.TFE_Execute.cs index 4aad851f..aa205d45 100644 --- a/src/TensorFlowNET.Core/Eager/EagerRunner.TFE_Execute.cs +++ b/src/TensorFlowNET.Core/Eager/EagerRunner.TFE_Execute.cs @@ -43,7 +43,7 @@ namespace Tensorflow.Eager { var status = tf.Status; var op = GetOp(ctx, op_name, status); - c_api.TFE_OpSetDevice(op, device_name, status.Handle); + c_api.TFE_OpSetDevice(op, device_name, status); if (status.ok()) { for (int i = 0; i < inputs.Length; ++i) @@ -54,7 +54,7 @@ namespace Tensorflow.Eager Tensor nd => nd.EagerTensorHandle, _ => throw new NotImplementedException("Eager tensor handle has not been allocated.") }; - c_api.TFE_OpAddInput(op, tensor_handle, status.Handle); + c_api.TFE_OpAddInput(op, tensor_handle, status); status.Check(true); } } @@ -64,7 +64,7 @@ namespace Tensorflow.Eager var outputs = new SafeEagerTensorHandle[num_outputs]; if (status.ok()) { - c_api.TFE_Execute(op, outputs, out num_outputs, status.Handle); + c_api.TFE_Execute(op, outputs, out num_outputs, status); status.Check(true); } return outputs.Select(x => new EagerTensor(x)).ToArray(); diff --git a/src/TensorFlowNET.Core/Eager/EagerRunner.TFE_FastPathExecute.cs b/src/TensorFlowNET.Core/Eager/EagerRunner.TFE_FastPathExecute.cs index c6158ab0..92d5b2a4 100644 --- a/src/TensorFlowNET.Core/Eager/EagerRunner.TFE_FastPathExecute.cs +++ b/src/TensorFlowNET.Core/Eager/EagerRunner.TFE_FastPathExecute.cs @@ -104,7 +104,7 @@ namespace Tensorflow.Eager var eager_tensor = ops.convert_to_tensor(fast_input_array[j]); attr_values[j] = eager_tensor.dtype; - c_api.TFE_OpAddInput(op, eager_tensor.EagerTensorHandle, status.Handle); + c_api.TFE_OpAddInput(op, eager_tensor.EagerTensorHandle, status); if (op_exec_info.run_callbacks) { @@ -142,7 +142,7 @@ namespace Tensorflow.Eager } var retVals = new SafeEagerTensorHandle[num_retvals]; - c_api.TFE_Execute(op, retVals, out num_retvals, status.Handle); + c_api.TFE_Execute(op, retVals, out num_retvals, status); status.Check(true); var flat_result = retVals.Select(x => new EagerTensor(x)).ToArray(); @@ -160,10 +160,10 @@ namespace Tensorflow.Eager SafeEagerOpHandle GetOp(Context ctx, string op_or_function_name, Status status) { if (thread_local_eager_operation_map.find(op_or_function_name, out var op)) - c_api.TFE_OpReset(op, op_or_function_name, ctx.DeviceName, status.Handle); + c_api.TFE_OpReset(op, op_or_function_name, ctx.DeviceName, status); else { - op = c_api.TFE_NewOp(ctx.Handle, op_or_function_name, status.Handle); + op = c_api.TFE_NewOp(ctx, op_or_function_name, status); thread_local_eager_operation_map[op_or_function_name] = op; } @@ -219,7 +219,7 @@ namespace Tensorflow.Eager flattened_attrs.Add(dtype); } - c_api.TFE_OpAddInput(op, tensor.EagerTensorHandle, status.Handle); + c_api.TFE_OpAddInput(op, tensor.EagerTensorHandle, status); status.Check(true); return true; @@ -235,7 +235,7 @@ namespace Tensorflow.Eager var value = attrs[i + 1]; byte is_list = 0; - var type = c_api.TFE_OpGetAttrType(op, key, ref is_list, status.Handle); + var type = c_api.TFE_OpGetAttrType(op, key, ref is_list, status); if (!status.ok()) return; if (is_list != 0) SetOpAttrList(tf.Context, op, key, value as object[], type, null, status); @@ -264,7 +264,7 @@ namespace Tensorflow.Eager Status status) { byte is_list = 0; - var type = c_api.TFE_OpGetAttrType(op, attr_name, ref is_list, status.Handle); + var type = c_api.TFE_OpGetAttrType(op, attr_name, ref is_list, status); if (status.Code != TF_Code.TF_OK) return; if (attr_value == null) @@ -305,7 +305,7 @@ namespace Tensorflow.Eager tf.memcpy(dims[i], values1[i].dims, values1[i].ndim * sizeof(long)); } - c_api.TFE_OpSetAttrShapeList(op, key, dims, num_dims, num_values, status.Handle); + c_api.TFE_OpSetAttrShapeList(op, key, dims, num_dims, num_values, status); Array.ForEach(dims, x => Marshal.FreeHGlobal(x)); } else if (type == TF_AttrType.TF_ATTR_TYPE && values is TF_DataType[] values2) @@ -353,7 +353,7 @@ namespace Tensorflow.Eager break; case TF_AttrType.TF_ATTR_SHAPE: var dims = (value as long[]).ToArray(); - c_api.TFE_OpSetAttrShape(op, key, dims, dims.Length, status.Handle); + c_api.TFE_OpSetAttrShape(op, key, dims, dims.Length, status); status.Check(true); break; case TF_AttrType.TF_ATTR_FUNC: diff --git a/src/TensorFlowNET.Core/Eager/EagerTensor.Creation.cs b/src/TensorFlowNET.Core/Eager/EagerTensor.Creation.cs index b9f741f3..c7d71de3 100644 --- a/src/TensorFlowNET.Core/Eager/EagerTensor.Creation.cs +++ b/src/TensorFlowNET.Core/Eager/EagerTensor.Creation.cs @@ -54,7 +54,7 @@ namespace Tensorflow.Eager void NewEagerTensorHandle(SafeTensorHandle h) { _id = ops.uid(); - _eagerTensorHandle = c_api.TFE_NewTensorHandle(h, tf.Status.Handle); + _eagerTensorHandle = c_api.TFE_NewTensorHandle(h, tf.Status); #if TRACK_TENSOR_LIFE Console.WriteLine($"New EagerTensor {_eagerTensorHandle}"); #endif @@ -65,7 +65,7 @@ namespace Tensorflow.Eager { if (_handle != null) return; - _handle = c_api.TFE_TensorHandleResolve(_eagerTensorHandle, tf.Status.Handle); + _handle = c_api.TFE_TensorHandleResolve(_eagerTensorHandle, tf.Status); tf.Status.Check(true); } diff --git a/src/TensorFlowNET.Core/Eager/EagerTensor.cs b/src/TensorFlowNET.Core/Eager/EagerTensor.cs index f85e8df6..02bd0bdf 100644 --- a/src/TensorFlowNET.Core/Eager/EagerTensor.cs +++ b/src/TensorFlowNET.Core/Eager/EagerTensor.cs @@ -24,10 +24,10 @@ namespace Tensorflow.Eager } } - public override string Device => c_api.StringPiece(c_api.TFE_TensorHandleDeviceName(_eagerTensorHandle, tf.Status.Handle)); + public override string Device => c_api.StringPiece(c_api.TFE_TensorHandleDeviceName(_eagerTensorHandle, tf.Status)); public override TF_DataType dtype => c_api.TFE_TensorHandleDataType(_eagerTensorHandle); - public override int rank => c_api.TFE_TensorHandleNumDims(EagerTensorHandle, tf.Status.Handle); + public override int rank => c_api.TFE_TensorHandleNumDims(EagerTensorHandle, tf.Status); public override ulong bytesize { @@ -49,9 +49,9 @@ namespace Tensorflow.Eager protected override Shape GetShapeInternal() { - var dims = new int[c_api.TFE_TensorHandleNumDims(_eagerTensorHandle, tf.Status.Handle)]; + var dims = new int[c_api.TFE_TensorHandleNumDims(_eagerTensorHandle, tf.Status)]; for (int i = 0; i < dims.Length; i++) - dims[i] = c_api.TFE_TensorHandleDim(_eagerTensorHandle, i, tf.Status.Handle); + dims[i] = c_api.TFE_TensorHandleDim(_eagerTensorHandle, i, tf.Status); return dims; } @@ -64,15 +64,15 @@ namespace Tensorflow.Eager public static int GetRank(IntPtr handle) { var tfe_tensor_handle = c_api.TFE_EagerTensorHandle(handle); - return c_api.TFE_TensorHandleNumDims(tfe_tensor_handle, tf.Status.Handle); + return c_api.TFE_TensorHandleNumDims(tfe_tensor_handle, tf.Status); } public static int[] GetDims(IntPtr handle) { var tfe_tensor_handle = c_api.TFE_EagerTensorHandle(handle); - var dims = new int[c_api.TFE_TensorHandleNumDims(tfe_tensor_handle, tf.Status.Handle)]; + var dims = new int[c_api.TFE_TensorHandleNumDims(tfe_tensor_handle, tf.Status)]; for (int i = 0; i < dims.Length; i++) - dims[i] = c_api.TFE_TensorHandleDim(tfe_tensor_handle, i, tf.Status.Handle); + dims[i] = c_api.TFE_TensorHandleDim(tfe_tensor_handle, i, tf.Status); return dims; } diff --git a/src/TensorFlowNET.Core/Eager/c_api.eager.cs b/src/TensorFlowNET.Core/Eager/c_api.eager.cs index d874ac93..6930b0c7 100644 --- a/src/TensorFlowNET.Core/Eager/c_api.eager.cs +++ b/src/TensorFlowNET.Core/Eager/c_api.eager.cs @@ -114,7 +114,7 @@ namespace Tensorflow /// /// [DllImport(TensorFlowLibName)] - public static extern void TFE_ContextAddFunction(SafeContextHandle ctx, IntPtr function, SafeStatusHandle status); + public static extern void TFE_ContextAddFunction(SafeContextHandle ctx, SafeFuncGraphHandle function, SafeStatusHandle status); /// /// Removes a function from the context. Once removed, you can no longer diff --git a/src/TensorFlowNET.Core/Framework/importer.cs b/src/TensorFlowNET.Core/Framework/importer.cs index 1d0098b4..5b99c200 100644 --- a/src/TensorFlowNET.Core/Framework/importer.cs +++ b/src/TensorFlowNET.Core/Framework/importer.cs @@ -56,15 +56,14 @@ namespace Tensorflow TF_ImportGraphDefResults results = null; var bytes = graph_def.ToByteString().ToArray(); - using (var buffer = c_api_util.tf_buffer(bytes)) - using (var scoped_options = c_api_util.ScopedTFImportGraphDefOptions()) - using (var status = new Status()) - { - _PopulateTFImportGraphDefOptions(scoped_options, prefix, input_map, return_elements); - // need to create a class ImportGraphDefWithResults with IDisposal - results = new TF_ImportGraphDefResults(c_api.TF_GraphImportGraphDefWithResults(graph, buffer.Handle, scoped_options.Handle, status.Handle)); - status.Check(true); - } + var buffer = c_api_util.tf_buffer(bytes); + var scoped_options = c_api_util.ScopedTFImportGraphDefOptions(); + var status = new Status(); + + _PopulateTFImportGraphDefOptions(scoped_options, prefix, input_map, return_elements); + // need to create a class ImportGraphDefWithResults with IDisposal + results = new TF_ImportGraphDefResults(c_api.TF_GraphImportGraphDefWithResults(graph, buffer, scoped_options, status)); + status.Check(true); _ProcessNewOps(graph); @@ -116,13 +115,13 @@ namespace Tensorflow Dictionary input_map, string[] return_elements) { - c_api.TF_ImportGraphDefOptionsSetPrefix(options.Handle, prefix); - c_api.TF_ImportGraphDefOptionsSetUniquifyNames(options.Handle, (char)1); + c_api.TF_ImportGraphDefOptionsSetPrefix(options, prefix); + c_api.TF_ImportGraphDefOptionsSetUniquifyNames(options, (char)1); foreach (var input in input_map) { var (src_name, src_index) = _ParseTensorName(input.Key); - c_api.TF_ImportGraphDefOptionsAddInputMapping(options.Handle, src_name, src_index, input.Value._as_tf_output()); + c_api.TF_ImportGraphDefOptionsAddInputMapping(options, src_name, src_index, input.Value._as_tf_output()); } if (return_elements == null) @@ -133,11 +132,11 @@ namespace Tensorflow if (name.Contains(":")) { var (op_name, index) = _ParseTensorName(name); - c_api.TF_ImportGraphDefOptionsAddReturnOutput(options.Handle, op_name, index); + c_api.TF_ImportGraphDefOptionsAddReturnOutput(options, op_name, index); } else { - c_api.TF_ImportGraphDefOptionsAddReturnOperation(options.Handle, name); + c_api.TF_ImportGraphDefOptionsAddReturnOperation(options, name); } } diff --git a/src/TensorFlowNET.Core/Framework/op_def_registry.py.cs b/src/TensorFlowNET.Core/Framework/op_def_registry.py.cs index eec234c6..111719aa 100644 --- a/src/TensorFlowNET.Core/Framework/op_def_registry.py.cs +++ b/src/TensorFlowNET.Core/Framework/op_def_registry.py.cs @@ -33,7 +33,7 @@ namespace Tensorflow if (_registered_ops.Count > 0) return _registered_ops; - using var buffer = new Buffer(c_api.TF_GetAllOpList()); + var buffer = new Buffer(c_api.TF_GetAllOpList()); var op_list = OpList.Parser.ParseFrom(buffer.ToArray()); foreach (var op_def in op_list.Op) _registered_ops[op_def.Name] = op_def; diff --git a/src/TensorFlowNET.Core/Framework/smart_module.cs b/src/TensorFlowNET.Core/Framework/smart_module.cs index d9e35a6d..e1f84d7e 100644 --- a/src/TensorFlowNET.Core/Framework/smart_module.cs +++ b/src/TensorFlowNET.Core/Framework/smart_module.cs @@ -56,8 +56,8 @@ namespace Tensorflow.Framework if (pred_value is null) { var result = range(pred.op.NumOutputs).Select(x => IntPtr.Zero).ToArray(); - var evaluated = c_api.TF_TryEvaluateConstant(pred.graph, pred._as_tf_output(), result, tf.Status.Handle); - if (!evaluated || c_api.TF_GetCode(tf.Status.Handle) != TF_Code.TF_OK) + var evaluated = c_api.TF_TryEvaluateConstant(pred.graph, pred._as_tf_output(), result, tf.Status); + if (!evaluated || c_api.TF_GetCode(tf.Status) != TF_Code.TF_OK) return null; else throw new NotImplementedException(""); diff --git a/src/TensorFlowNET.Core/Functions/c_api.function.cs b/src/TensorFlowNET.Core/Functions/c_api.function.cs index 230d85ba..3fbb3868 100644 --- a/src/TensorFlowNET.Core/Functions/c_api.function.cs +++ b/src/TensorFlowNET.Core/Functions/c_api.function.cs @@ -34,10 +34,10 @@ namespace Tensorflow /// /// [DllImport(TensorFlowLibName)] - public static extern void TF_FunctionToFunctionDef(IntPtr func, SafeBufferHandle output_func_def, SafeStatusHandle status); + public static extern void TF_FunctionToFunctionDef(SafeFuncGraphHandle func, SafeBufferHandle output_func_def, SafeStatusHandle status); [DllImport(TensorFlowLibName)] - public static extern IntPtr TF_GraphToFunction(IntPtr fn_body, string fn_name, + public static extern SafeFuncGraphHandle TF_GraphToFunction(SafeGraphHandle fn_body, string fn_name, bool append_hash_to_fn_name, int num_opers, IntPtr[] opers, int ninputs, TF_Output[] inputs, @@ -48,12 +48,12 @@ namespace Tensorflow SafeStatusHandle status); [DllImport(TensorFlowLibName)] - public static extern IntPtr TF_FunctionSetAttrValueProto(IntPtr func, string attr_name, byte[] proto, int proto_len, SafeStatusHandle status); + public static extern IntPtr TF_FunctionSetAttrValueProto(SafeFuncGraphHandle func, string attr_name, byte[] proto, int proto_len, SafeStatusHandle status); [DllImport(TensorFlowLibName)] - public static extern IntPtr TF_FunctionName(IntPtr func); + public static extern IntPtr TF_FunctionName(SafeFuncGraphHandle func); [DllImport(TensorFlowLibName)] - public static extern void TF_GraphCopyFunction(IntPtr g, IntPtr func, IntPtr grad, SafeStatusHandle status); + public static extern void TF_GraphCopyFunction(SafeGraphHandle g, SafeFuncGraphHandle func, IntPtr grad, SafeStatusHandle status); } } diff --git a/src/TensorFlowNET.Core/Gradients/c_api.gradient.cs b/src/TensorFlowNET.Core/Gradients/c_api.gradient.cs index 70dcfd67..901a33ca 100644 --- a/src/TensorFlowNET.Core/Gradients/c_api.gradient.cs +++ b/src/TensorFlowNET.Core/Gradients/c_api.gradient.cs @@ -37,7 +37,7 @@ namespace Tensorflow /// TF_Status* /// TF_Output* [DllImport(TensorFlowLibName)] - public static extern void TF_AddGradientsWithPrefix(IntPtr g, string prefix, TF_Output[] y, int ny, + public static extern void TF_AddGradientsWithPrefix(SafeGraphHandle g, string prefix, TF_Output[] y, int ny, TF_Output[] x, int nx, TF_Output[] dx, SafeStatusHandle status, IntPtr[] dy); } } diff --git a/src/TensorFlowNET.Core/GraphTransformation/GraphTransformer.cs b/src/TensorFlowNET.Core/GraphTransformation/GraphTransformer.cs index 8870e295..f662b448 100644 --- a/src/TensorFlowNET.Core/GraphTransformation/GraphTransformer.cs +++ b/src/TensorFlowNET.Core/GraphTransformation/GraphTransformer.cs @@ -22,21 +22,19 @@ namespace Tensorflow var inputs_string = string.Join(",", inputs); var outputs_string = string.Join(",", outputs); var transforms_string = string.Join(" ", transforms); - using (var status = new Status()) - { - var buffer = new Buffer(); - var len = c_api.TransformGraphWithStringInputs(input_graph_def_string, - input_graph_def_string.Length, - inputs_string, - outputs_string, - transforms_string, - buffer.Handle, - status.Handle); + var status = new Status(); + var buffer = new Buffer(); + var len = c_api.TransformGraphWithStringInputs(input_graph_def_string, + input_graph_def_string.Length, + inputs_string, + outputs_string, + transforms_string, + buffer, + status); - status.Check(false); - var bytes = buffer.ToArray(); - return GraphDef.Parser.ParseFrom(bytes); - } + status.Check(false); + var bytes = buffer.ToArray(); + return GraphDef.Parser.ParseFrom(bytes); } } } diff --git a/src/TensorFlowNET.Core/Graphs/AutoGraph.cs b/src/TensorFlowNET.Core/Graphs/AutoGraph.cs index ceeca8ab..48d14d6b 100644 --- a/src/TensorFlowNET.Core/Graphs/AutoGraph.cs +++ b/src/TensorFlowNET.Core/Graphs/AutoGraph.cs @@ -37,11 +37,9 @@ namespace Tensorflow.Graphs 1); return result[0]; } - using (var s = tf.Session(input.graph)) - { - var output = func(input); - return output; - } + var s = tf.Session(input.graph); + var output = func(input); + return output; }; } @@ -75,12 +73,10 @@ namespace Tensorflow.Graphs 1); return result[0]; } - using (var s = tf.Session(a.graph)) - { - Debug.Assert(a.graph == b.graph); - var output = func(a, b); - return output; - } + var s = tf.Session(a.graph); + Debug.Assert(a.graph == b.graph); + var output = func(a, b); + return output; }; } } diff --git a/src/TensorFlowNET.Core/Graphs/FuncGraph.cs b/src/TensorFlowNET.Core/Graphs/FuncGraph.cs index df750813..a8dd4eb9 100644 --- a/src/TensorFlowNET.Core/Graphs/FuncGraph.cs +++ b/src/TensorFlowNET.Core/Graphs/FuncGraph.cs @@ -1,258 +1,252 @@ using Google.Protobuf; -using System; -using System.Collections.Generic; -using System.Linq; using Tensorflow.Eager; using Tensorflow.Exceptions; using static Tensorflow.Binding; -namespace Tensorflow.Graphs +namespace Tensorflow.Graphs; + +/// +/// Graph representing a function body. +/// +public class FuncGraph : Graph, IDisposable { + SafeFuncGraphHandle _func_graph_handle; + public string FuncName => _graph_key; + + public Tensors Inputs { get; set; } = new Tensors(); + public Tensors Outputs { get; set; } = new Tensors(); + public Dictionary Attrs { get; set; } + + Dictionary _captures + = new Dictionary(); + + public Tensor[] external_captures + => _captures.Select(x => x.Value.Item1).ToArray(); + public (Tensor, Tensor)[] captures + => _captures.Values.Select(x => x).ToArray(); + + public Tensor[] internal_captures + => _captures.Select(x => x.Value.Item2).ToArray(); + + public Tensor[] captured_inputs + => external_captures; + /// - /// Graph representing a function body. + /// Construct a new FuncGraph. /// - public class FuncGraph : Graph + public FuncGraph(string name) : base() { - IntPtr _func_graph_handle; - public string FuncName => _graph_key; - - public Tensors Inputs { get; set; } = new Tensors(); - public Tensors Outputs { get; set; } = new Tensors(); - public Dictionary Attrs { get; set; } + outer_graph = ops.get_default_graph(); + while (outer_graph.building_function) + outer_graph = outer_graph.OuterGraph; + _graph_key = name; + building_function = true; + } - Dictionary _captures - = new Dictionary(); + public FuncGraph(SafeGraphHandle handle, string name, Dictionary attrs) : base() + { + outer_graph = ops.get_default_graph(); + while (outer_graph.building_function) + outer_graph = outer_graph.OuterGraph; + _graph_key = name; + building_function = true; + Attrs = attrs; + // Will to test if FuncGraph has memory leak + // c_api.TF_DeleteGraph(_handle); + _handle = handle; + } - public Tensor[] external_captures - => _captures.Select(x => x.Value.Item1).ToArray(); - public (Tensor, Tensor)[] captures - => _captures.Values.Select(x => x).ToArray(); + public void ToGraph(Operation[] opers, + Tensor[] inputs, Tensor[] outputs, + string[] output_names) + { + var status = new Status(); + _func_graph_handle = c_api.TF_GraphToFunction(_handle, + _graph_key, + false, + opers.Length, + opers.Select(x => (IntPtr)x).ToArray(), + inputs.Length, + inputs.Select(x => new TF_Output(x.op, 0)).ToArray(), + outputs.Length, + outputs.Select(x => new TF_Output(x.op, 0)).ToArray(), + output_names, + IntPtr.Zero, + null, + status); + status.Check(true); + + SetAttrs(); + + // c_api.TF_GraphCopyFunction(outer_graph, _func_graph_handle, IntPtr.Zero, status.Handle); + // status.Check(true); + + c_api.TFE_ContextAddFunction(tf.Context, _func_graph_handle, status); + status.Check(true); + + _graph_key = c_api.StringPiece(c_api.TF_FunctionName(_func_graph_handle)); + + Inputs = inputs; + // mark_as_return + Outputs = outputs;// .Select(x => array_ops.identity(x)).ToArray(); + } - public Tensor[] internal_captures - => _captures.Select(x => x.Value.Item2).ToArray(); + public override Operation create_op(string op_type, Tensor[] inputs, TF_DataType[] dtypes, TF_DataType[] input_types = null, string name = null, Dictionary attrs = null, OpDef op_def = null, bool compute_device = true) + { + foreach(var (i, inp) in enumerate(inputs)) + inputs[i] = capture(inp); - public Tensor[] captured_inputs - => external_captures; + return base.create_op(op_type, inputs, dtypes, input_types, name, attrs, op_def, compute_device); + } - /// - /// Construct a new FuncGraph. - /// - public FuncGraph(string name) : base() + const int _EAGER_CONST_THRESHOLD = 128; + public Tensor capture(Tensor tensor, string name = null, Shape shape = null) + { + if(tensor is EagerTensor) { - outer_graph = ops.get_default_graph(); - while (outer_graph.building_function) - outer_graph = outer_graph.OuterGraph; - _graph_key = name; - building_function = true; + if (name == null) + name = ops.uid().ToString(); + + // Small EagerTensors are captured with Const ops + if (dtypes.is_value_dtype(tensor.dtype) + && (tensor.rank == 0 || tensor.size < _EAGER_CONST_THRESHOLD)) + return capture_eager_tensor(tensor, name); + + // Large EagerTensors and resources are captured with Placeholder ops + return _capture_helper(tensor, name, shape: shape); } - public FuncGraph(IntPtr handle, string name, Dictionary attrs) : base() + if(tensor.graph != this) { - outer_graph = ops.get_default_graph(); - while (outer_graph.building_function) - outer_graph = outer_graph.OuterGraph; - _graph_key = name; - building_function = true; - Attrs = attrs; - // Will to test if FuncGraph has memory leak - // c_api.TF_DeleteGraph(_handle); - _handle = handle; + if (name == null) + name = tensor.op.name; + var inner_graph = tensor.graph; + while(inner_graph != null && inner_graph is FuncGraph inner_func_graph) + { + if (inner_graph == this) + throw new InaccessibleTensorError($"The tensor '{tensor.name}' cannot be accessed here: it is defined" + + " in another function or code block. Use return values," + + " explicit Python locals or TensorFlow collections to access" + + $" it. Defined in: {tensor.graph.graph_key}; accessed from: {graph_key}."); + inner_graph = inner_func_graph.outer_graph; + } + return _capture_helper(tensor, name); } - public void ToGraph(Operation[] opers, - Tensor[] inputs, Tensor[] outputs, - string[] output_names) + return tensor; + } + + Tensor capture_eager_tensor(Tensor tensor, string name) + { + Tensor graph_const = null; + if (!_captures.ContainsKey(tensor.Id)) { - var status = new Status(); - _func_graph_handle = c_api.TF_GraphToFunction(_handle, - _graph_key, - false, - opers.Length, - opers.Select(x => (IntPtr)x).ToArray(), - inputs.Length, - inputs.Select(x => new TF_Output(x.op, 0)).ToArray(), - outputs.Length, - outputs.Select(x => new TF_Output(x.op, 0)).ToArray(), - output_names == null || output_names.Length == 0 ? null : output_names, - IntPtr.Zero, - null, - status.Handle); - status.Check(true); - - SetAttrs(); - - // c_api.TF_GraphCopyFunction(outer_graph, _func_graph_handle, IntPtr.Zero, status.Handle); - // status.Check(true); - - c_api.TFE_ContextAddFunction(tf.Context.Handle, _func_graph_handle, status.Handle); - status.Check(true); - - _graph_key = c_api.StringPiece(c_api.TF_FunctionName(_func_graph_handle)); - - Inputs = inputs; - // mark_as_return - Outputs = outputs;// .Select(x => array_ops.identity(x)).ToArray(); + graph_const = tf_with(ops.control_dependencies(null), ctl + => constant_op.constant(tensor.numpy(), dtype: tensor.dtype, shape: tensor.shape, name: name)); + add_capture(tensor, graph_const); } - - public override Operation create_op(string op_type, Tensor[] inputs, TF_DataType[] dtypes, TF_DataType[] input_types = null, string name = null, Dictionary attrs = null, OpDef op_def = null, bool compute_device = true) + else { - foreach(var (i, inp) in enumerate(inputs)) - inputs[i] = capture(inp); - - return base.create_op(op_type, inputs, dtypes, input_types, name, attrs, op_def, compute_device); + graph_const = _captures[tensor.Id].Item2; } - const int _EAGER_CONST_THRESHOLD = 128; - public Tensor capture(Tensor tensor, string name = null, Shape shape = null) + BackwardFunction _backward_function_wrapper = (output_grads, unneeded_gradients) => { - if(tensor is EagerTensor) - { - if (name == null) - name = ops.uid().ToString(); - - // Small EagerTensors are captured with Const ops - if (dtypes.is_value_dtype(tensor.dtype) - && (tensor.rank == 0 || tensor.size < _EAGER_CONST_THRESHOLD)) - return capture_eager_tensor(tensor, name); + return output_grads; + }; - // Large EagerTensors and resources are captured with Placeholder ops - return _capture_helper(tensor, name, shape: shape); - } + tf.Runner.RecordGradient("captured_value", + new[] { graph_const }, null, + new[] { tensor }, + getBackwardFunction: _backward_function_wrapper + /*getForwardFunction: forward_function*/); - if(tensor.graph != this) - { - if (name == null) - name = tensor.op.name; - var inner_graph = tensor.graph; - while(inner_graph != null && inner_graph is FuncGraph inner_func_graph) - { - if (inner_graph == this) - throw new InaccessibleTensorError($"The tensor '{tensor.name}' cannot be accessed here: it is defined" + - " in another function or code block. Use return values," + - " explicit Python locals or TensorFlow collections to access" + - $" it. Defined in: {tensor.graph.graph_key}; accessed from: {graph_key}."); - inner_graph = inner_func_graph.outer_graph; - } - return _capture_helper(tensor, name); - } + return graph_const; + } - return tensor; + Tensor _capture_helper(Tensor tensor, string name, Shape shape = null) + { + Tensor placeholder = null; + if (!_captures.ContainsKey(tensor.Id)) + { + placeholder = _create_substitute_placeholder(tensor, + name: name, + dtype: tensor.dtype, + shape: shape); + add_capture(tensor, placeholder); } - - Tensor capture_eager_tensor(Tensor tensor, string name) + else { - Tensor graph_const = null; - if (!_captures.ContainsKey(tensor.Id)) - { - graph_const = tf_with(ops.control_dependencies(null), ctl - => constant_op.constant(tensor.numpy(), dtype: tensor.dtype, shape: tensor.shape, name: name)); - add_capture(tensor, graph_const); - } - else - { - graph_const = _captures[tensor.Id].Item2; - } - - BackwardFunction _backward_function_wrapper = (output_grads, unneeded_gradients) => - { - return output_grads; - }; - - tf.Runner.RecordGradient("captured_value", - new[] { graph_const }, null, - new[] { tensor }, - getBackwardFunction: _backward_function_wrapper - /*getForwardFunction: forward_function*/); - - return graph_const; + placeholder = _captures[tensor.Id].Item2; } - Tensor _capture_helper(Tensor tensor, string name, Shape shape = null) + BackwardFunction _backward_function_wrapper = (output_grads, unneeded_gradients) => { - Tensor placeholder = null; - if (!_captures.ContainsKey(tensor.Id)) - { - placeholder = _create_substitute_placeholder(tensor, - name: name, - dtype: tensor.dtype, - shape: shape); - add_capture(tensor, placeholder); - } - else - { - placeholder = _captures[tensor.Id].Item2; - } + return output_grads; + }; - BackwardFunction _backward_function_wrapper = (output_grads, unneeded_gradients) => - { - return output_grads; - }; + tf.Runner.RecordGradient("captured_value", + new[] { placeholder }, null, + new[] { tensor }, + getBackwardFunction: _backward_function_wrapper + /*getForwardFunction: forward_function*/); - tf.Runner.RecordGradient("captured_value", - new[] { placeholder }, null, - new[] { tensor }, - getBackwardFunction: _backward_function_wrapper - /*getForwardFunction: forward_function*/); + return placeholder; + } - return placeholder; - } + void add_capture(Tensor tensor, Tensor placeholder) + { + _captures.Add(tensor.Id, (tensor, placeholder)); + Inputs.Add(placeholder); + } - void add_capture(Tensor tensor, Tensor placeholder) - { - _captures.Add(tensor.Id, (tensor, placeholder)); - Inputs.Add(placeholder); - } + Tensor _create_substitute_placeholder(Tensor value, + string name = null, + TF_DataType dtype = TF_DataType.DtInvalid, + Shape shape = null) + { + if (shape is null) + shape = value.shape; + if (dtype == TF_DataType.DtInvalid) + dtype = value.dtype; + + var placeholder = tf_with(ops.control_dependencies(null), ctl + => array_ops.placeholder(dtype, shape: shape, name: name)); + // custom_gradient.copy_handle_data(value, placeholder) + return placeholder; + } - Tensor _create_substitute_placeholder(Tensor value, - string name = null, - TF_DataType dtype = TF_DataType.DtInvalid, - Shape shape = null) - { - if (shape is null) - shape = value.shape; - if (dtype == TF_DataType.DtInvalid) - dtype = value.dtype; - - var placeholder = tf_with(ops.control_dependencies(null), ctl - => array_ops.placeholder(dtype, shape: shape, name: name)); - // custom_gradient.copy_handle_data(value, placeholder) - return placeholder; - } + void SetAttrs() + { + if (Attrs == null) + return; - void SetAttrs() + foreach (var (_name, attr_value) in enumerate(Attrs)) { - if (Attrs == null) - return; - - foreach (var (_name, attr_value) in enumerate(Attrs)) + var serialized = new AttrValue { - var serialized = new AttrValue - { - S = ByteString.CopyFromUtf8(attr_value) - }.ToByteArray(); - c_api.TF_FunctionSetAttrValueProto(_func_graph_handle, _name, serialized, serialized.Length, tf.Status.Handle); - tf.Status.Check(true); - } + S = ByteString.CopyFromUtf8(attr_value) + }.ToByteArray(); + c_api.TF_FunctionSetAttrValueProto(_func_graph_handle, _name, serialized, serialized.Length, tf.Status); + tf.Status.Check(true); } + } - public override Graph as_default() - { - tf.Context.graph_mode(isFunc: true); - ops.set_default_graph(this); - return this; - } + public override Graph as_default() + { + tf.Context.graph_mode(isFunc: true); + ops.set_default_graph(this); + return this; + } - public override void Exit() - { - tf.Context.restore_mode(); - ops.pop_graph(); - } + public override void Exit() + { + tf.Context.restore_mode(); + ops.pop_graph(); + } - protected override void DisposeUnmanagedResources(IntPtr handle) - { - c_api.TFE_ContextRemoveFunction(tf.Context.Handle, _graph_key, tf.Status.Handle); - c_api.TF_DeleteFunction(_func_graph_handle); - base.DisposeUnmanagedResources(handle); - } + public void Dispose() + { + c_api.TFE_ContextRemoveFunction(tf.Context, _graph_key, tf.Status); } } diff --git a/src/TensorFlowNET.Core/Graphs/Graph.Export.cs b/src/TensorFlowNET.Core/Graphs/Graph.Export.cs index 612c7401..a11d91e7 100644 --- a/src/TensorFlowNET.Core/Graphs/Graph.Export.cs +++ b/src/TensorFlowNET.Core/Graphs/Graph.Export.cs @@ -24,7 +24,7 @@ namespace Tensorflow public Buffer ToGraphDef(Status s) { var buffer = new Buffer(); - c_api.TF_GraphToGraphDef(_handle, buffer.Handle, s.Handle); + c_api.TF_GraphToGraphDef(_handle, buffer, s); s.Check(true); return buffer; @@ -33,14 +33,12 @@ namespace Tensorflow private GraphDef _as_graph_def(bool add_shapes = false) { GraphDef def; - using (var status = new Status()) - using (var buffer = ToGraphDef(status)) - { - status.Check(true); - // limit size to 250M, recursion to max 100 - var inputStream = CodedInputStream.CreateWithLimits(buffer.DangerousMemoryBlock, 250 * 1024 * 1024, 100); - def = GraphDef.Parser.ParseFrom(inputStream); - } + var status = new Status(); + var buffer = ToGraphDef(status); + status.Check(true); + // limit size to 250M, recursion to max 100 + var inputStream = CodedInputStream.CreateWithLimits(buffer.DangerousMemoryBlock, 250 * 1024 * 1024, 100); + def = GraphDef.Parser.ParseFrom(inputStream); // Strip the experimental library field iff it's empty. // if(def.Library.Function.Count == 0) diff --git a/src/TensorFlowNET.Core/Graphs/Graph.Import.cs b/src/TensorFlowNET.Core/Graphs/Graph.Import.cs index 28ecd64e..b80e2659 100644 --- a/src/TensorFlowNET.Core/Graphs/Graph.Import.cs +++ b/src/TensorFlowNET.Core/Graphs/Graph.Import.cs @@ -29,7 +29,7 @@ namespace Tensorflow int size = Marshal.SizeOf(); var return_output_handle = Marshal.AllocHGlobal(size * num_return_outputs); - c_api.TF_GraphImportGraphDefWithReturnOutputs(_handle, graph_def.Handle, opts.Handle, return_output_handle, num_return_outputs, s.Handle); + c_api.TF_GraphImportGraphDefWithReturnOutputs(_handle, graph_def, opts, return_output_handle, num_return_outputs, s); var tf_output_ptr = (TF_Output*)return_output_handle; for (int i = 0; i < num_return_outputs; i++) @@ -48,15 +48,14 @@ namespace Tensorflow public bool Import(byte[] bytes, string prefix = "") { - using (var opts = new ImportGraphDefOptions()) - using (var status = new Status()) - using (var graph_def = new Buffer(bytes)) - { - c_api.TF_ImportGraphDefOptionsSetPrefix(opts.Handle, prefix); - c_api.TF_GraphImportGraphDef(_handle, graph_def.Handle, opts.Handle, status.Handle); - status.Check(true); - return status.Code == TF_Code.TF_OK; - } + var opts = new ImportGraphDefOptions(); + var status = new Status(); + var graph_def = new Buffer(bytes); + + c_api.TF_ImportGraphDefOptionsSetPrefix(opts, prefix); + c_api.TF_GraphImportGraphDef(_handle, graph_def, opts, status); + status.Check(true); + return status.Code == TF_Code.TF_OK; } public Graph ImportGraphDef(string file_path, string name = null) diff --git a/src/TensorFlowNET.Core/Graphs/Graph.cs b/src/TensorFlowNET.Core/Graphs/Graph.cs index 2a982274..98cad3b2 100644 --- a/src/TensorFlowNET.Core/Graphs/Graph.cs +++ b/src/TensorFlowNET.Core/Graphs/Graph.cs @@ -75,9 +75,9 @@ namespace Tensorflow /// then create a TensorFlow session to run parts of the graph across a set of local and remote devices. /// /// https://www.tensorflow.org/guide/graphs

https://www.tensorflow.org/api_docs/python/tf/Graph - public partial class Graph : DisposableObject - , IEnumerable + public partial class Graph : IEnumerable { + protected new SafeGraphHandle _handle; private Dictionary _nodes_by_id; public Dictionary _nodes_by_name; private Dictionary _names_in_use; @@ -130,15 +130,6 @@ namespace Tensorflow _graph_key = $"graph-{ops.GraphUniqueId()}/"; } - public Graph(IntPtr handle) - { - _handle = handle; - _nodes_by_id = new Dictionary(); - _nodes_by_name = new Dictionary(); - _names_in_use = new Dictionary(); - _graph_key = $"grap-{ops.GraphUniqueId()}/"; - } - public ITensorOrOperation as_graph_element(object obj, bool allow_tensor = true, bool allow_operation = true) { return _as_graph_element_locked(obj, allow_tensor, allow_operation); @@ -486,16 +477,6 @@ namespace Tensorflow _unfetchable_ops.Add(op); } - protected override void DisposeManagedResources() - { - - } - - protected override void DisposeUnmanagedResources(IntPtr handle) - { - c_api.TF_DeleteGraph(handle); - } - public Tensor get_tensor_by_tf_output(TF_Output tf_output) { var op = _get_operation_by_tf_operation(tf_output.oper); @@ -517,14 +498,14 @@ namespace Tensorflow public Shape GetTensorShape(TF_Output output) { var status = tf.Status; - var ndim = c_api.TF_GraphGetTensorNumDims(_handle, output, status.Handle); + var ndim = c_api.TF_GraphGetTensorNumDims(_handle, output, status); status.Check(); if (ndim == -1) return Shape.Null; var dims = new long[ndim]; - c_api.TF_GraphGetTensorShape(_handle, output, dims, dims.Length, status.Handle); + c_api.TF_GraphGetTensorShape(_handle, output, dims, dims.Length, status); status.Check(); return new Shape(dims.Select(x => (int)x).ToArray()); @@ -539,7 +520,7 @@ namespace Tensorflow string debugString = string.Empty; public override string ToString() { - return $"{graph_key}, 0x{_handle.ToString("x16")}"; + return $"{graph_key}, 0x{_handle.DangerousGetHandle().ToString("x16")}"; /*if (string.IsNullOrEmpty(debugString)) { int len = 0; @@ -558,7 +539,7 @@ namespace Tensorflow IEnumerator IEnumerable.GetEnumerator() => throw new NotImplementedException(); - public static implicit operator IntPtr(Graph graph) + public static implicit operator SafeGraphHandle(Graph graph) { return graph._handle; } diff --git a/src/TensorFlowNET.Core/Graphs/ImportGraphDefOptions.cs b/src/TensorFlowNET.Core/Graphs/ImportGraphDefOptions.cs index a04cf55a..859465fc 100644 --- a/src/TensorFlowNET.Core/Graphs/ImportGraphDefOptions.cs +++ b/src/TensorFlowNET.Core/Graphs/ImportGraphDefOptions.cs @@ -14,28 +14,27 @@ limitations under the License. ******************************************************************************/ -using System; +namespace Tensorflow; -namespace Tensorflow +public sealed class ImportGraphDefOptions { - public sealed class ImportGraphDefOptions : IDisposable - { - public SafeImportGraphDefOptionsHandle Handle { get; } + SafeImportGraphDefOptionsHandle _handle { get; } - public int NumReturnOutputs - => c_api.TF_ImportGraphDefOptionsNumReturnOutputs(Handle); + public int NumReturnOutputs + => c_api.TF_ImportGraphDefOptionsNumReturnOutputs(_handle); - public ImportGraphDefOptions() - { - Handle = c_api.TF_NewImportGraphDefOptions(); - } + public ImportGraphDefOptions() + { + _handle = c_api.TF_NewImportGraphDefOptions(); + } - public void AddReturnOutput(string name, int index) - { - c_api.TF_ImportGraphDefOptionsAddReturnOutput(Handle, name, index); - } + public void AddReturnOutput(string name, int index) + { + c_api.TF_ImportGraphDefOptionsAddReturnOutput(_handle, name, index); + } - public void Dispose() - => Handle.Dispose(); + public static implicit operator SafeImportGraphDefOptionsHandle(ImportGraphDefOptions opt) + { + return opt._handle; } } diff --git a/src/TensorFlowNET.Core/Graphs/SafeFuncGraphHandle.cs b/src/TensorFlowNET.Core/Graphs/SafeFuncGraphHandle.cs new file mode 100644 index 00000000..f38301b6 --- /dev/null +++ b/src/TensorFlowNET.Core/Graphs/SafeFuncGraphHandle.cs @@ -0,0 +1,22 @@ +using Tensorflow.Util; + +namespace Tensorflow; + +public sealed class SafeFuncGraphHandle : SafeTensorflowHandle +{ + private SafeFuncGraphHandle() + { + } + + public SafeFuncGraphHandle(IntPtr handle) + : base(handle) + { + } + + protected override bool ReleaseHandle() + { + c_api.TF_DeleteFunction(handle); + SetHandle(IntPtr.Zero); + return true; + } +} diff --git a/src/TensorFlowNET.Core/Graphs/SafeGraphHandle.cs b/src/TensorFlowNET.Core/Graphs/SafeGraphHandle.cs new file mode 100644 index 00000000..a6da0198 --- /dev/null +++ b/src/TensorFlowNET.Core/Graphs/SafeGraphHandle.cs @@ -0,0 +1,22 @@ +using Tensorflow.Util; + +namespace Tensorflow; + +public sealed class SafeGraphHandle : SafeTensorflowHandle +{ + private SafeGraphHandle() + { + } + + public SafeGraphHandle(IntPtr handle) + : base(handle) + { + } + + protected override bool ReleaseHandle() + { + c_api.TF_DeleteGraph(handle); + SetHandle(IntPtr.Zero); + return true; + } +} diff --git a/src/TensorFlowNET.Core/Graphs/c_api.graph.cs b/src/TensorFlowNET.Core/Graphs/c_api.graph.cs index 6eb8f367..dc1827d8 100644 --- a/src/TensorFlowNET.Core/Graphs/c_api.graph.cs +++ b/src/TensorFlowNET.Core/Graphs/c_api.graph.cs @@ -60,7 +60,7 @@ namespace Tensorflow /// /// [DllImport(TensorFlowLibName)] - public static extern void TF_GraphGetTensorShape(IntPtr graph, TF_Output output, long[] dims, int num_dims, SafeStatusHandle status); + public static extern void TF_GraphGetTensorShape(SafeGraphHandle graph, TF_Output output, long[] dims, int num_dims, SafeStatusHandle status); /// /// Import the graph serialized in `graph_def` into `graph`. @@ -78,7 +78,7 @@ namespace Tensorflow /// int /// TF_Status* [DllImport(TensorFlowLibName)] - public static extern unsafe void TF_GraphImportGraphDefWithReturnOutputs(IntPtr graph, SafeBufferHandle graph_def, SafeImportGraphDefOptionsHandle options, IntPtr return_outputs, int num_return_outputs, SafeStatusHandle status); + public static extern unsafe void TF_GraphImportGraphDefWithReturnOutputs(SafeGraphHandle graph, SafeBufferHandle graph_def, SafeImportGraphDefOptionsHandle options, IntPtr return_outputs, int num_return_outputs, SafeStatusHandle status); /// /// Import the graph serialized in `graph_def` into `graph`. Returns nullptr and @@ -92,7 +92,7 @@ namespace Tensorflow /// TF_Status* /// TF_ImportGraphDefResults* [DllImport(TensorFlowLibName)] - public static extern SafeImportGraphDefResultsHandle TF_GraphImportGraphDefWithResults(IntPtr graph, SafeBufferHandle graph_def, SafeImportGraphDefOptionsHandle options, SafeStatusHandle status); + public static extern SafeImportGraphDefResultsHandle TF_GraphImportGraphDefWithResults(SafeGraphHandle graph, SafeBufferHandle graph_def, SafeImportGraphDefOptionsHandle options, SafeStatusHandle status); /// /// Import the graph serialized in `graph_def` into `graph`. @@ -102,7 +102,7 @@ namespace Tensorflow /// TF_ImportGraphDefOptions* /// TF_Status* [DllImport(TensorFlowLibName)] - public static extern void TF_GraphImportGraphDef(IntPtr graph, SafeBufferHandle graph_def, SafeImportGraphDefOptionsHandle options, SafeStatusHandle status); + public static extern void TF_GraphImportGraphDef(SafeGraphHandle graph, SafeBufferHandle graph_def, SafeImportGraphDefOptionsHandle options, SafeStatusHandle status); /// /// Iterate through the operations of a graph. @@ -111,7 +111,7 @@ namespace Tensorflow /// /// [DllImport(TensorFlowLibName)] - public static extern IntPtr TF_GraphNextOperation(IntPtr graph, ref uint pos); + public static extern IntPtr TF_GraphNextOperation(SafeGraphHandle graph, ref uint pos); /// /// Returns the operation in the graph with `oper_name`. Returns nullptr if @@ -121,14 +121,14 @@ namespace Tensorflow /// /// [DllImport(TensorFlowLibName)] - public static extern IntPtr TF_GraphOperationByName(IntPtr graph, string oper_name); + public static extern IntPtr TF_GraphOperationByName(SafeGraphHandle graph, string oper_name); /// /// Sets the shape of the Tensor referenced by `output` in `graph` to /// the shape described by `dims` and `num_dims`. /// [DllImport(TensorFlowLibName)] - public static extern void TF_GraphSetTensorShape(IntPtr graph, TF_Output output, long[] dims, int num_dims, SafeStatusHandle status); + public static extern void TF_GraphSetTensorShape(SafeGraphHandle graph, TF_Output output, long[] dims, int num_dims, SafeStatusHandle status); /// /// Write out a serialized representation of `graph` (as a GraphDef protocol @@ -138,7 +138,7 @@ namespace Tensorflow /// TF_Buffer* /// TF_Status* [DllImport(TensorFlowLibName)] - public static extern void TF_GraphToGraphDef(IntPtr graph, SafeBufferHandle output_graph_def, SafeStatusHandle status); + public static extern void TF_GraphToGraphDef(SafeGraphHandle graph, SafeBufferHandle output_graph_def, SafeStatusHandle status); /// /// Returns the number of dimensions of the Tensor referenced by `output` @@ -151,7 +151,7 @@ namespace Tensorflow /// /// [DllImport(TensorFlowLibName)] - public static extern int TF_GraphGetTensorNumDims(IntPtr graph, TF_Output output, SafeStatusHandle status); + public static extern int TF_GraphGetTensorNumDims(SafeGraphHandle graph, TF_Output output, SafeStatusHandle status); /// /// Cause the imported graph to have a control dependency on `oper`. `oper` @@ -287,12 +287,12 @@ namespace Tensorflow /// TF_Status* /// [DllImport(TensorFlowLibName)] - public static extern IntPtr TF_LoadSessionFromSavedModel(SafeSessionOptionsHandle session_options, IntPtr run_options, + public static extern SafeSessionHandle TF_LoadSessionFromSavedModel(SafeSessionOptionsHandle session_options, IntPtr run_options, string export_dir, string[] tags, int tags_len, - IntPtr graph, IntPtr meta_graph_def, SafeStatusHandle status); + SafeGraphHandle graph, IntPtr meta_graph_def, SafeStatusHandle status); [DllImport(TensorFlowLibName)] - public static extern IntPtr TF_NewGraph(); + public static extern SafeGraphHandle TF_NewGraph(); [DllImport(TensorFlowLibName)] public static extern SafeImportGraphDefOptionsHandle TF_NewImportGraphDefOptions(); @@ -334,6 +334,6 @@ namespace Tensorflow /// /// [DllImport(TensorFlowLibName)] - public static extern bool TF_TryEvaluateConstant(IntPtr graph, TF_Output output, IntPtr[] result, SafeStatusHandle status); + public static extern bool TF_TryEvaluateConstant(SafeGraphHandle graph, TF_Output output, IntPtr[] result, SafeStatusHandle status); } } diff --git a/src/TensorFlowNET.Core/Numpy/NDArray.Creation.cs b/src/TensorFlowNET.Core/Numpy/NDArray.Creation.cs index 7417476e..d9743ead 100644 --- a/src/TensorFlowNET.Core/Numpy/NDArray.Creation.cs +++ b/src/TensorFlowNET.Core/Numpy/NDArray.Creation.cs @@ -61,7 +61,7 @@ namespace Tensorflow.NumPy { if (_handle is not null) { - _eagerTensorHandle = c_api.TFE_NewTensorHandle(_handle, tf.Status.Handle); + _eagerTensorHandle = c_api.TFE_NewTensorHandle(_handle, tf.Status); } } } diff --git a/src/TensorFlowNET.Core/Operations/Operation.Input.cs b/src/TensorFlowNET.Core/Operations/Operation.Input.cs index 44ac52e1..9aa6fde2 100644 --- a/src/TensorFlowNET.Core/Operations/Operation.Input.cs +++ b/src/TensorFlowNET.Core/Operations/Operation.Input.cs @@ -31,7 +31,7 @@ namespace Tensorflow public int InputListLength(string name) { int num = 0; - num = c_api.TF_OperationInputListLength(_handle, name, tf.Status.Handle); + num = c_api.TF_OperationInputListLength(_handle, name, tf.Status); tf.Status.Check(true); return num; } diff --git a/src/TensorFlowNET.Core/Operations/Operation.Output.cs b/src/TensorFlowNET.Core/Operations/Operation.Output.cs index b5d6191d..2955a13f 100644 --- a/src/TensorFlowNET.Core/Operations/Operation.Output.cs +++ b/src/TensorFlowNET.Core/Operations/Operation.Output.cs @@ -28,7 +28,7 @@ namespace Tensorflow public int OutputListLength(string name) { - int num = c_api.TF_OperationOutputListLength(_handle, name, tf.Status.Handle); + int num = c_api.TF_OperationOutputListLength(_handle, name, tf.Status); tf.Status.Check(true); return num; diff --git a/src/TensorFlowNET.Core/Operations/Operation.cs b/src/TensorFlowNET.Core/Operations/Operation.cs index fb9a4a27..751ade5d 100644 --- a/src/TensorFlowNET.Core/Operations/Operation.cs +++ b/src/TensorFlowNET.Core/Operations/Operation.cs @@ -187,8 +187,8 @@ namespace Tensorflow if (tf.executing_eagerly()) return (T[])get_attr(name); - using var buf = new Buffer(); - c_api.TF_OperationGetAttrValueProto(_handle, name, buf.Handle, tf.Status.Handle); + var buf = new Buffer(); + c_api.TF_OperationGetAttrValueProto(_handle, name, buf, tf.Status); tf.Status.Check(true); var x = AttrValue.Parser.ParseFrom(buf.ToArray()); @@ -210,8 +210,8 @@ namespace Tensorflow public virtual object get_attr(string name) { - using var buf = new Buffer(); - c_api.TF_OperationGetAttrValueProto(_handle, name, buf.Handle, tf.Status.Handle); + var buf = new Buffer(); + c_api.TF_OperationGetAttrValueProto(_handle, name, buf, tf.Status); tf.Status.Check(true); var x = AttrValue.Parser.ParseFrom(buf.ToArray()); @@ -235,13 +235,13 @@ namespace Tensorflow public TF_AttrMetadata GetAttributeMetadata(string attr_name, Status s) { - return c_api.TF_OperationGetAttrMetadata(_handle, attr_name, s.Handle); + return c_api.TF_OperationGetAttrMetadata(_handle, attr_name, s); } private NodeDef GetNodeDef() { - using var buffer = new Buffer(); - c_api.TF_OperationToNodeDef(_handle, buffer.Handle, tf.Status.Handle); + var buffer = new Buffer(); + c_api.TF_OperationToNodeDef(_handle, buffer, tf.Status); tf.Status.Check(throwException: true); return NodeDef.Parser.ParseFrom(buffer.ToArray()); } diff --git a/src/TensorFlowNET.Core/Operations/OperationDescription.cs b/src/TensorFlowNET.Core/Operations/OperationDescription.cs index 384f5386..28df548d 100644 --- a/src/TensorFlowNET.Core/Operations/OperationDescription.cs +++ b/src/TensorFlowNET.Core/Operations/OperationDescription.cs @@ -50,7 +50,7 @@ namespace Tensorflow public Operation FinishOperation(Status status) { - return c_api.TF_FinishOperation(_handle, status.Handle); + return c_api.TF_FinishOperation(_handle, status); } public static implicit operator OperationDescription(IntPtr handle) diff --git a/src/TensorFlowNET.Core/Operations/c_api.ops.cs b/src/TensorFlowNET.Core/Operations/c_api.ops.cs index 0fc92454..900db8ca 100644 --- a/src/TensorFlowNET.Core/Operations/c_api.ops.cs +++ b/src/TensorFlowNET.Core/Operations/c_api.ops.cs @@ -96,7 +96,7 @@ namespace Tensorflow /// const char* /// TF_OperationDescription* [DllImport(TensorFlowLibName)] - public static extern IntPtr TF_NewOperation(IntPtr graph, string opType, string oper_name); + public static extern IntPtr TF_NewOperation(SafeGraphHandle graph, string opType, string oper_name); [DllImport(TensorFlowLibName)] public static extern IntPtr TF_OperationDevice(IntPtr oper); diff --git a/src/TensorFlowNET.Core/Sessions/BaseSession.cs b/src/TensorFlowNET.Core/Sessions/BaseSession.cs index 4e131b36..0a9cfc2e 100644 --- a/src/TensorFlowNET.Core/Sessions/BaseSession.cs +++ b/src/TensorFlowNET.Core/Sessions/BaseSession.cs @@ -14,281 +14,272 @@ limitations under the License. ******************************************************************************/ -using Google.Protobuf; using Tensorflow.NumPy; -using System; -using System.Collections; -using System.Collections.Generic; -using System.Linq; -using System.Numerics; -using System.Text; -using Tensorflow.Util; using static Tensorflow.Binding; -namespace Tensorflow +namespace Tensorflow; + +public class BaseSession : IDisposable { - public class BaseSession : DisposableObject + protected SafeSessionHandle _handle; + protected Graph _graph; + protected Status _status; + public Graph graph => _graph; + + public BaseSession(SafeSessionHandle handle, Graph g) { - protected Graph _graph; - protected Status _status; - public Graph graph => _graph; + _handle = handle; + _graph = g ?? ops.get_default_graph(); + } - public BaseSession(IntPtr handle, Graph g) + public BaseSession(string target = "", Graph g = null, ConfigProto config = null, Status status = null) + { + _graph = g ?? ops.get_default_graph(); + if (!_graph.building_function) { - _handle = handle; - _graph = g ?? ops.get_default_graph(); + if (ops.get_default_graph() != _graph) + _graph.as_default(); } + + var opts = new SessionOptions(target, config); + _status = status ?? tf.Status; + _handle = c_api.TF_NewSession(_graph, opts, _status); + _status.Check(true); + } - public BaseSession(string target = "", Graph g = null, ConfigProto config = null, Status status = null) - { - _graph = g ?? ops.get_default_graph(); - if (!_graph.building_function) - { - if (ops.get_default_graph() != _graph) - _graph.as_default(); - } - - using var opts = new SessionOptions(target, config); - _status = status ?? tf.Status; - _handle = c_api.TF_NewSession(_graph, opts.Handle, _status.Handle); - _status.Check(true); - } + public virtual void run(Operation op, params FeedItem[] feed_dict) + { + _run(op, feed_dict); + } - public virtual void run(Operation op, params FeedItem[] feed_dict) - { - _run(op, feed_dict); - } + public virtual NDArray run(Tensor fetche, params FeedItem[] feed_dict) + { + return _run(fetche, feed_dict)[0]; + } - public virtual NDArray run(Tensor fetche, params FeedItem[] feed_dict) - { - return _run(fetche, feed_dict)[0]; - } + public virtual NDArray run(ITensorOrOperation fetche, params FeedItem[] feed_dict) + { + var results = _run(fetche, feed_dict); + return fetche is Tensor ? results[0] : null; + } - public virtual NDArray run(ITensorOrOperation fetche, params FeedItem[] feed_dict) - { - var results = _run(fetche, feed_dict); - return fetche is Tensor ? results[0] : null; - } + public virtual (NDArray, NDArray, NDArray, NDArray, NDArray) run( + (ITensorOrOperation, ITensorOrOperation, ITensorOrOperation, ITensorOrOperation, ITensorOrOperation) fetches, + params FeedItem[] feed_dict) + { + var results = _run(new object[] { fetches.Item1, fetches.Item2, fetches.Item3, fetches.Item4, fetches.Item5 }, feed_dict); + return (results[0], results[1], results[2], results[3], results[4]); + } - public virtual (NDArray, NDArray, NDArray, NDArray, NDArray) run( - (ITensorOrOperation, ITensorOrOperation, ITensorOrOperation, ITensorOrOperation, ITensorOrOperation) fetches, - params FeedItem[] feed_dict) - { - var results = _run(new object[] { fetches.Item1, fetches.Item2, fetches.Item3, fetches.Item4, fetches.Item5 }, feed_dict); - return (results[0], results[1], results[2], results[3], results[4]); - } + public virtual (NDArray, NDArray, NDArray, NDArray) run((ITensorOrOperation, ITensorOrOperation, ITensorOrOperation, ITensorOrOperation) fetches, params FeedItem[] feed_dict) + { + var results = _run(new object[] { fetches.Item1, fetches.Item2, fetches.Item3, fetches.Item4 }, feed_dict); + return (results[0], results[1], results[2], results[3]); + } - public virtual (NDArray, NDArray, NDArray, NDArray) run((ITensorOrOperation, ITensorOrOperation, ITensorOrOperation, ITensorOrOperation) fetches, params FeedItem[] feed_dict) - { - var results = _run(new object[] { fetches.Item1, fetches.Item2, fetches.Item3, fetches.Item4 }, feed_dict); - return (results[0], results[1], results[2], results[3]); - } + public virtual (NDArray, NDArray, NDArray) run((ITensorOrOperation, ITensorOrOperation, ITensorOrOperation) fetches, params FeedItem[] feed_dict) + { + var results = _run(new object[] { fetches.Item1, fetches.Item2, fetches.Item3 }, feed_dict); + return (results[0], results[1], results[2]); + } - public virtual (NDArray, NDArray, NDArray) run((ITensorOrOperation, ITensorOrOperation, ITensorOrOperation) fetches, params FeedItem[] feed_dict) - { - var results = _run(new object[] { fetches.Item1, fetches.Item2, fetches.Item3 }, feed_dict); - return (results[0], results[1], results[2]); - } + public virtual (NDArray, NDArray) run((ITensorOrOperation, ITensorOrOperation) fetches, params FeedItem[] feed_dict) + { + var results = _run(new object[] { fetches.Item1, fetches.Item2 }, feed_dict); + return (results[0], results[1]); + } - public virtual (NDArray, NDArray) run((ITensorOrOperation, ITensorOrOperation) fetches, params FeedItem[] feed_dict) - { - var results = _run(new object[] { fetches.Item1, fetches.Item2 }, feed_dict); - return (results[0], results[1]); - } + public virtual NDArray[] run(object fetches, params FeedItem[] feed_dict) + { + return _run(fetches, feed_dict); + } - public virtual NDArray[] run(object fetches, params FeedItem[] feed_dict) - { - return _run(fetches, feed_dict); - } + public virtual NDArray[] run(object fetches, Hashtable feed_dict = null) + { + var feed_items = feed_dict == null ? new FeedItem[0] : feed_dict.Keys.OfType().Select(key => new FeedItem(key, feed_dict[key])).ToArray(); + return _run(fetches, feed_items); + } - public virtual NDArray[] run(object fetches, Hashtable feed_dict = null) - { - var feed_items = feed_dict == null ? new FeedItem[0] : feed_dict.Keys.OfType().Select(key => new FeedItem(key, feed_dict[key])).ToArray(); - return _run(fetches, feed_items); - } + private NDArray[] _run(object fetches, FeedItem[] feed_dict = null) + { + var feed_dict_tensor = new Dictionary(); + //var feed_map = new Dictionary(); - private NDArray[] _run(object fetches, FeedItem[] feed_dict = null) + // Validate and process feed_dict. + if (feed_dict != null) { - var feed_dict_tensor = new Dictionary(); - //var feed_map = new Dictionary(); - - // Validate and process feed_dict. - if (feed_dict != null) + foreach (var subfeed in feed_dict) { - foreach (var subfeed in feed_dict) - { - var subfeed_t = _graph.as_graph_element(subfeed.Key, allow_tensor: true, allow_operation: false); - //var target_dtype = subfeed_t.dtype.as_numpy_typecode(); // subfeed_dtype was never used - feed_dict_tensor[subfeed_t] = subfeed.Value; - //feed_map[subfeed_t.name] = (subfeed_t, subfeed.Value); - } + var subfeed_t = _graph.as_graph_element(subfeed.Key, allow_tensor: true, allow_operation: false); + //var target_dtype = subfeed_t.dtype.as_numpy_typecode(); // subfeed_dtype was never used + feed_dict_tensor[subfeed_t] = subfeed.Value; + //feed_map[subfeed_t.name] = (subfeed_t, subfeed.Value); } + } - // Create a fetch handler to take care of the structure of fetches. - var fetch_handler = new _FetchHandler(_graph, fetches, feed_dict_tensor); + // Create a fetch handler to take care of the structure of fetches. + var fetch_handler = new _FetchHandler(_graph, fetches, feed_dict_tensor); - // Run request and get response. - // We need to keep the returned movers alive for the following _do_run(). - // These movers are no longer needed when _do_run() completes, and - // are deleted when `movers` goes out of scope when this _run() ends. - var _ = _update_with_movers(); - var final_fetches = fetch_handler.fetches(); - var final_targets = fetch_handler.targets(); + // Run request and get response. + // We need to keep the returned movers alive for the following _do_run(). + // These movers are no longer needed when _do_run() completes, and + // are deleted when `movers` goes out of scope when this _run() ends. + var _ = _update_with_movers(); + var final_fetches = fetch_handler.fetches(); + var final_targets = fetch_handler.targets(); - // We only want to really perform the run if fetches or targets are provided, - // or if the call is a partial run that specifies feeds. - var results = _do_run(final_targets.Select(x => (Operation)x).ToList(), final_fetches, feed_dict_tensor); + // We only want to really perform the run if fetches or targets are provided, + // or if the call is a partial run that specifies feeds. + var results = _do_run(final_targets.Select(x => (Operation)x).ToList(), final_fetches, feed_dict_tensor); - return fetch_handler.build_results(this, results); - } + return fetch_handler.build_results(this, results); + } - /// - /// Runs a step based on the given fetches and feeds. - /// - /// A list of operations to be run, but not fetched. - /// - /// - /// - /// A list of numpy ndarrays, corresponding to the elements of - /// `fetch_list`. If the ith element of `fetch_list` contains the - /// name of an operation, the first Tensor output of that operation - /// will be returned for that element. - /// - private NDArray[] _do_run(List target_list, List fetch_list, Dictionary feed_dict) + /// + /// Runs a step based on the given fetches and feeds. + /// + /// A list of operations to be run, but not fetched. + /// + /// + /// + /// A list of numpy ndarrays, corresponding to the elements of + /// `fetch_list`. If the ith element of `fetch_list` contains the + /// name of an operation, the first Tensor output of that operation + /// will be returned for that element. + /// + private NDArray[] _do_run(List target_list, List fetch_list, Dictionary feed_dict) + { + var feeds = new KeyValuePair[feed_dict.Count]; + int i = 0; + foreach (var x in feed_dict) { - var feeds = new KeyValuePair[feed_dict.Count]; - int i = 0; - foreach (var x in feed_dict) + if (x.Key is Tensor key) { - if (x.Key is Tensor key) + switch (x.Value) { - switch (x.Value) - { - case Tensor v: - if (v.dtype != key.dtype) - throw new ValueError($"Tensor {v} does not match the expected dtype {key.dtype}, actual dtype: {v.dtype}"); - feeds[i++] = new KeyValuePair(key._as_tf_output(), v); - break; - case SafeTensorHandle v: - var tensor = new Tensor(v); - if (tensor.dtype != key.dtype) - throw new ValueError($"Tensor {v} does not match the expected dtype {key.dtype}, actual dtype: {tensor.dtype}"); - feeds[i++] = new KeyValuePair(key._as_tf_output(), tensor); - break; - case bool v: - feeds[i++] = new KeyValuePair(key._as_tf_output(), new Tensor(v)); - break; - case byte v: - feeds[i++] = new KeyValuePair(key._as_tf_output(), new Tensor(v)); - break; - case int v: - feeds[i++] = new KeyValuePair(key._as_tf_output(), new Tensor(v)); - break; - case long v: - feeds[i++] = new KeyValuePair(key._as_tf_output(), new Tensor(v)); - break; - case float v: - feeds[i++] = new KeyValuePair(key._as_tf_output(), new Tensor(v)); - break; - case double v: - feeds[i++] = new KeyValuePair(key._as_tf_output(), new Tensor(v)); - break; - case string v: - feeds[i++] = new KeyValuePair(key._as_tf_output(), new Tensor(v)); - break; - case Array v: - feeds[i++] = new KeyValuePair(key._as_tf_output(), new Tensor(v, v.GetShape())); - break; - default: - throw new NotImplementedException(""); - } + case Tensor v: + if (v.dtype != key.dtype) + throw new ValueError($"Tensor {v} does not match the expected dtype {key.dtype}, actual dtype: {v.dtype}"); + feeds[i++] = new KeyValuePair(key._as_tf_output(), v); + break; + case SafeTensorHandle v: + var tensor = new Tensor(v); + if (tensor.dtype != key.dtype) + throw new ValueError($"Tensor {v} does not match the expected dtype {key.dtype}, actual dtype: {tensor.dtype}"); + feeds[i++] = new KeyValuePair(key._as_tf_output(), tensor); + break; + case bool v: + feeds[i++] = new KeyValuePair(key._as_tf_output(), new Tensor(v)); + break; + case byte v: + feeds[i++] = new KeyValuePair(key._as_tf_output(), new Tensor(v)); + break; + case int v: + feeds[i++] = new KeyValuePair(key._as_tf_output(), new Tensor(v)); + break; + case long v: + feeds[i++] = new KeyValuePair(key._as_tf_output(), new Tensor(v)); + break; + case float v: + feeds[i++] = new KeyValuePair(key._as_tf_output(), new Tensor(v)); + break; + case double v: + feeds[i++] = new KeyValuePair(key._as_tf_output(), new Tensor(v)); + break; + case string v: + feeds[i++] = new KeyValuePair(key._as_tf_output(), new Tensor(v)); + break; + case Array v: + feeds[i++] = new KeyValuePair(key._as_tf_output(), new Tensor(v, v.GetShape())); + break; + default: + throw new NotImplementedException(""); } - else - throw new NotImplementedException(""); } - - var fetches = fetch_list.Select(x => x._as_tf_output()).ToArray(); - //var targets = target_list; - return _call_tf_sessionrun(feeds, fetches, target_list); + else + throw new NotImplementedException(""); } + var fetches = fetch_list.Select(x => x._as_tf_output()).ToArray(); + //var targets = target_list; + return _call_tf_sessionrun(feeds, fetches, target_list); + } - private unsafe NDArray[] _call_tf_sessionrun(KeyValuePair[] feed_dict, TF_Output[] fetch_list, List target_list) - { - // Ensure any changes to the graph are reflected in the runtime. - _extend_graph(); - var output_values = fetch_list.Select(x => IntPtr.Zero).ToArray(); + private unsafe NDArray[] _call_tf_sessionrun(KeyValuePair[] feed_dict, TF_Output[] fetch_list, List target_list) + { + // Ensure any changes to the graph are reflected in the runtime. + _extend_graph(); - c_api.TF_SessionRun(_handle, - run_options: null, - inputs: feed_dict.Select(f => f.Key).ToArray(), - input_values: feed_dict.Select(f => f.Value.Handle.DangerousGetHandle()).ToArray(), - ninputs: feed_dict.Length, - outputs: fetch_list, - output_values: output_values, - noutputs: fetch_list.Length, - target_opers: target_list.Select(f => (IntPtr)f).ToArray(), - ntargets: target_list.Count, - run_metadata: IntPtr.Zero, - status: _status.Handle); + var output_values = fetch_list.Select(x => IntPtr.Zero).ToArray(); - _status.Check(true); + c_api.TF_SessionRun(_handle, + run_options: null, + inputs: feed_dict.Select(f => f.Key).ToArray(), + input_values: feed_dict.Select(f => f.Value.Handle.DangerousGetHandle()).ToArray(), + ninputs: feed_dict.Length, + outputs: fetch_list, + output_values: output_values, + noutputs: fetch_list.Length, + target_opers: target_list.Select(f => (IntPtr)f).ToArray(), + ntargets: target_list.Count, + run_metadata: IntPtr.Zero, + status: _status); - var result = new NDArray[fetch_list.Length]; + _status.Check(true); - for (int i = 0; i < fetch_list.Length; i++) - result[i] = fetchValue(new SafeTensorHandle(output_values[i])); + var result = new NDArray[fetch_list.Length]; - return result; - } + for (int i = 0; i < fetch_list.Length; i++) + result[i] = fetchValue(new SafeTensorHandle(output_values[i])); - public unsafe Tensor eval(Tensor tensor) - { - var output_values = new IntPtr[1]; - var fetch_list = new[] { tensor._as_tf_output() }; - - c_api.TF_SessionRun(_handle, - run_options: null, - inputs: new TF_Output[0], - input_values: new IntPtr[0], - ninputs: 0, - outputs: fetch_list, - output_values: output_values, - noutputs: 1, - target_opers: new IntPtr[0], - ntargets: 0, - run_metadata: IntPtr.Zero, - status: _status.Handle); - - _status.Check(true); - - return new Tensor(new SafeTensorHandle(output_values[0])); - } + return result; + } - private static unsafe NDArray fetchValue(SafeTensorHandle output) - { - var tensor = new Tensor(output); - return tensor.numpy(); - } + public unsafe Tensor eval(Tensor tensor) + { + var output_values = new IntPtr[1]; + var fetch_list = new[] { tensor._as_tf_output() }; + + c_api.TF_SessionRun(_handle, + run_options: null, + inputs: new TF_Output[0], + input_values: new IntPtr[0], + ninputs: 0, + outputs: fetch_list, + output_values: output_values, + noutputs: 1, + target_opers: new IntPtr[0], + ntargets: 0, + run_metadata: IntPtr.Zero, + status: _status); + + _status.Check(true); + + return new Tensor(new SafeTensorHandle(output_values[0])); + } - /// - /// If a tensor handle that is fed to a device incompatible placeholder, - /// we move the tensor to the right device, generate a new tensor handle, - /// and update feed_dict to use the new handle. - /// - private List _update_with_movers() - { - return new List { }; - } + private static unsafe NDArray fetchValue(SafeTensorHandle output) + { + var tensor = new Tensor(output); + return tensor.numpy(); + } - private void _extend_graph() - { } + /// + /// If a tensor handle that is fed to a device incompatible placeholder, + /// we move the tensor to the right device, generate a new tensor handle, + /// and update feed_dict to use the new handle. + /// + private List _update_with_movers() + { + return new List { }; + } - protected override void DisposeUnmanagedResources(IntPtr handle) - { - // c_api.TF_CloseSession(handle, tf.Status.Handle); - c_api.TF_DeleteSession(handle, _status.Handle); - } + private void _extend_graph() + { } + + public void Dispose() + { + } } diff --git a/src/TensorFlowNET.Core/Sessions/SafeSessionHandle.cs b/src/TensorFlowNET.Core/Sessions/SafeSessionHandle.cs new file mode 100644 index 00000000..4e4b013c --- /dev/null +++ b/src/TensorFlowNET.Core/Sessions/SafeSessionHandle.cs @@ -0,0 +1,46 @@ +/***************************************************************************** + Copyright 2018 The TensorFlow.NET Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. +******************************************************************************/ + +using System; +using System.Net.NetworkInformation; +using Tensorflow.Util; + +namespace Tensorflow +{ + public sealed class SafeSessionHandle : SafeTensorflowHandle + { + private SafeSessionHandle() + { + } + + public SafeSessionHandle(IntPtr handle) + : base(handle) + { + } + + public override string ToString() + => $"0x{handle:x16}"; + + protected override bool ReleaseHandle() + { + var status = new Status(); + // c_api.TF_CloseSession(handle, tf.Status.Handle); + c_api.TF_DeleteSession(handle, status); + SetHandle(IntPtr.Zero); + return true; + } + } +} diff --git a/src/TensorFlowNET.Core/Sessions/Session.cs b/src/TensorFlowNET.Core/Sessions/Session.cs index 05178fb8..3b91b489 100644 --- a/src/TensorFlowNET.Core/Sessions/Session.cs +++ b/src/TensorFlowNET.Core/Sessions/Session.cs @@ -14,75 +14,49 @@ limitations under the License. ******************************************************************************/ -using System; -using System.IO; -using System.Runtime.CompilerServices; -using Tensorflow.Util; +namespace Tensorflow; -namespace Tensorflow +public class Session : BaseSession { - public class Session : BaseSession - { - public Session(string target = "", Graph g = null) : base(target, g, null) - { } - - public Session(IntPtr handle, Graph g = null) : base(handle, g) - { } - - public Session(Graph g, ConfigProto config = null, Status s = null) : base("", g, config, s) - { } - - public Session as_default() - { - return ops.set_default_session(this); - } - - public static Session LoadFromSavedModel(string path) - { - var graph = new Graph(); - using var status = new Status(); - using var opt = c_api.TF_NewSessionOptions(); - - var tags = new string[] { "serve" }; - - var sess = c_api.TF_LoadSessionFromSavedModel(opt, - IntPtr.Zero, - path, - tags, - tags.Length, - graph, - IntPtr.Zero, - status.Handle); - status.Check(true); - - // load graph bytes - // var data = new byte[buffer.length]; - // Marshal.Copy(buffer.data, data, 0, (int)buffer.length); - // var meta_graph = MetaGraphDef.Parser.ParseFrom(data);*/ - return new Session(sess, g: graph); - } - - public static implicit operator IntPtr(Session session) => session._handle; - public static implicit operator Session(IntPtr handle) => new Session(handle); + public Session(string target = "", Graph g = null) : base(target, g, null) + { } - public void __enter__() - { + public Session(SafeSessionHandle handle, Graph g = null) : base(handle, g) + { } - } + public Session(Graph g, ConfigProto config = null, Status s = null) : base("", g, config, s) + { } - public void __exit__() - { - - } - - public void __init__() - { - - } - - public void __del__() - { + public Session as_default() + { + return ops.set_default_session(this); + } - } + public static Session LoadFromSavedModel(string path) + { + var graph = new Graph(); + var status = new Status(); + using var opt = c_api.TF_NewSessionOptions(); + + var tags = new string[] { "serve" }; + + var sess = c_api.TF_LoadSessionFromSavedModel(opt, + IntPtr.Zero, + path, + tags, + tags.Length, + graph, + IntPtr.Zero, + status); + status.Check(true); + + // load graph bytes + // var data = new byte[buffer.length]; + // Marshal.Copy(buffer.data, data, 0, (int)buffer.length); + // var meta_graph = MetaGraphDef.Parser.ParseFrom(data);*/ + return new Session(sess, g: graph); } + + public static implicit operator SafeSessionHandle(Session session) => session._handle; + public static implicit operator Session(SafeSessionHandle handle) => new Session(handle); } diff --git a/src/TensorFlowNET.Core/Sessions/SessionOptions.cs b/src/TensorFlowNET.Core/Sessions/SessionOptions.cs index 00923d14..4a11a7f9 100644 --- a/src/TensorFlowNET.Core/Sessions/SessionOptions.cs +++ b/src/TensorFlowNET.Core/Sessions/SessionOptions.cs @@ -19,33 +19,33 @@ using System; namespace Tensorflow { - internal sealed class SessionOptions : IDisposable + internal sealed class SessionOptions { - public SafeSessionOptionsHandle Handle { get; } + SafeSessionOptionsHandle _handle { get; } public SessionOptions(string target = "", ConfigProto config = null) { - Handle = c_api.TF_NewSessionOptions(); - c_api.TF_SetTarget(Handle, target); + _handle = c_api.TF_NewSessionOptions(); + c_api.TF_SetTarget(_handle, target); if (config != null) SetConfig(config); } - public void Dispose() - => Handle.Dispose(); - private unsafe void SetConfig(ConfigProto config) { var bytes = config.ToByteArray(); fixed (byte* proto2 = bytes) { - using (var status = new Status()) - { - c_api.TF_SetConfig(Handle, (IntPtr)proto2, (ulong)bytes.Length, status.Handle); - status.Check(false); - } + var status = new Status(); + c_api.TF_SetConfig(_handle, (IntPtr)proto2, (ulong)bytes.Length, status); + status.Check(false); } } + + public static implicit operator SafeSessionOptionsHandle(SessionOptions opt) + { + return opt._handle; + } } } diff --git a/src/TensorFlowNET.Core/Sessions/c_api.session.cs b/src/TensorFlowNET.Core/Sessions/c_api.session.cs index 548d79e7..a26ab56d 100644 --- a/src/TensorFlowNET.Core/Sessions/c_api.session.cs +++ b/src/TensorFlowNET.Core/Sessions/c_api.session.cs @@ -62,7 +62,7 @@ namespace Tensorflow /// TF_Status* /// TF_Session* [DllImport(TensorFlowLibName)] - public static extern IntPtr TF_NewSession(IntPtr graph, SafeSessionOptionsHandle opts, SafeStatusHandle status); + public static extern SafeSessionHandle TF_NewSession(SafeGraphHandle graph, SafeSessionOptionsHandle opts, SafeStatusHandle status); /// /// Return a new options object. @@ -110,7 +110,7 @@ namespace Tensorflow /// TF_Buffer* /// TF_Status* [DllImport(TensorFlowLibName)] - public static extern unsafe void TF_SessionRun(IntPtr session, TF_Buffer* run_options, + public static extern unsafe void TF_SessionRun(SafeSessionHandle session, TF_Buffer* run_options, TF_Output[] inputs, IntPtr[] input_values, int ninputs, TF_Output[] outputs, IntPtr[] output_values, int noutputs, IntPtr[] target_opers, int ntargets, diff --git a/src/TensorFlowNET.Core/Status/Status.cs b/src/TensorFlowNET.Core/Status/Status.cs index 288297fb..a890c2ae 100644 --- a/src/TensorFlowNET.Core/Status/Status.cs +++ b/src/TensorFlowNET.Core/Status/Status.cs @@ -26,7 +26,7 @@ namespace Tensorflow /// TF_Status holds error information. It either has an OK code, or /// else an error code with an associated error message. /// - public sealed class Status : IDisposable + public sealed class Status { /// /// Error message @@ -35,9 +35,9 @@ namespace Tensorflow { get { - using (Handle.Lease()) + using (_handle.Lease()) { - return StringPiece(TF_Message(Handle)); + return StringPiece(TF_Message(_handle)); } } } @@ -45,23 +45,23 @@ namespace Tensorflow /// /// Error code /// - public TF_Code Code => TF_GetCode(Handle); + public TF_Code Code => TF_GetCode(_handle); - public SafeStatusHandle Handle { get; } + SafeStatusHandle _handle { get; } public Status() { - Handle = TF_NewStatus(); + _handle = TF_NewStatus(); } public Status(SafeStatusHandle handle) { - Handle = handle ?? throw new ArgumentNullException(nameof(handle)); + _handle = handle ?? throw new ArgumentNullException(nameof(handle)); } public void SetStatus(TF_Code code, string msg) { - TF_SetStatus(Handle, code, msg); + TF_SetStatus(_handle, code, msg); } public bool ok() => Code == TF_Code.TF_OK; @@ -94,10 +94,12 @@ namespace Tensorflow } } - public void Dispose() - => Handle.Dispose(); - public override string ToString() - => $"{Code} 0x{Handle.DangerousGetHandle():x16}"; + => $"{Code} 0x{_handle.DangerousGetHandle():x16}"; + + public static implicit operator SafeStatusHandle(Status status) + { + return status._handle; + } } } \ No newline at end of file diff --git a/src/TensorFlowNET.Core/Tensors/Tensor.cs b/src/TensorFlowNET.Core/Tensors/Tensor.cs index e9ab81a7..ade00d5c 100644 --- a/src/TensorFlowNET.Core/Tensors/Tensor.cs +++ b/src/TensorFlowNET.Core/Tensors/Tensor.cs @@ -121,7 +121,7 @@ namespace Tensorflow if (_handle == null) { - c_api.TF_GraphGetTensorShape(op.graph, _as_tf_output(), dims, rank, tf.Status.Handle); + c_api.TF_GraphGetTensorShape(op.graph, _as_tf_output(), dims, rank, tf.Status); } else { @@ -135,9 +135,9 @@ namespace Tensorflow protected virtual void SetShapeInternal(Shape value) { if (value == null) - c_api.TF_GraphSetTensorShape(graph, _as_tf_output(), null, -1, tf.Status.Handle); + c_api.TF_GraphSetTensorShape(graph, _as_tf_output(), null, -1, tf.Status); else - c_api.TF_GraphSetTensorShape(graph, _as_tf_output(), value.dims, value.ndim, tf.Status.Handle); + c_api.TF_GraphSetTensorShape(graph, _as_tf_output(), value.dims, value.ndim, tf.Status); } public int[] _shape_tuple() @@ -176,7 +176,7 @@ namespace Tensorflow if (_handle == null) { var output = _as_tf_output(); - int ndim = c_api.TF_GraphGetTensorNumDims(op.graph, output, tf.Status.Handle); + int ndim = c_api.TF_GraphGetTensorNumDims(op.graph, output, tf.Status); return ndim; } diff --git a/src/TensorFlowNET.Core/Training/Saving/saver.py.cs b/src/TensorFlowNET.Core/Training/Saving/saver.py.cs index 4f583600..f94f9894 100644 --- a/src/TensorFlowNET.Core/Training/Saving/saver.py.cs +++ b/src/TensorFlowNET.Core/Training/Saving/saver.py.cs @@ -94,18 +94,16 @@ namespace Tensorflow string output_pb = Path.GetFullPath(Path.Combine(checkpoint_dir, "../", $"{output_pb_name}.pb")); - using (var graph = tf.Graph()) - using (var sess = tf.Session(graph)) - { - var saver = tf.train.import_meta_graph($"{checkpoint}.meta", clear_devices: true); - saver.restore(sess, checkpoint); - var output_graph_def = tf.graph_util.convert_variables_to_constants(sess, - graph.as_graph_def(), - output_node_names); - Binding.tf_output_redirect.WriteLine($"Froze {output_graph_def.Node.Count} nodes."); - File.WriteAllBytes(output_pb, output_graph_def.ToByteArray()); - return output_pb; - } + var graph = tf.Graph(); + var sess = tf.Session(graph); + var saver = tf.train.import_meta_graph($"{checkpoint}.meta", clear_devices: true); + saver.restore(sess, checkpoint); + var output_graph_def = tf.graph_util.convert_variables_to_constants(sess, + graph.as_graph_def(), + output_node_names); + Binding.tf_output_redirect.WriteLine($"Froze {output_graph_def.Node.Count} nodes."); + File.WriteAllBytes(output_pb, output_graph_def.ToByteArray()); + return output_pb; } public static Graph load_graph(string freeze_graph_pb, string name = "") diff --git a/src/TensorFlowNET.Core/Variables/BaseResourceVariable.cs b/src/TensorFlowNET.Core/Variables/BaseResourceVariable.cs index 9b8cfcb5..9427b87f 100644 --- a/src/TensorFlowNET.Core/Variables/BaseResourceVariable.cs +++ b/src/TensorFlowNET.Core/Variables/BaseResourceVariable.cs @@ -164,7 +164,7 @@ namespace Tensorflow result._as_tf_output(), shape.dims, shape.ndim, - tf.Status.Handle); + tf.Status); tf.Status.Check(true); } diff --git a/src/TensorFlowNET.Core/ops.cs b/src/TensorFlowNET.Core/ops.cs index bf5ae7be..48d8b5c5 100644 --- a/src/TensorFlowNET.Core/ops.cs +++ b/src/TensorFlowNET.Core/ops.cs @@ -247,7 +247,7 @@ namespace Tensorflow foreach (var attr in node_def.Attr) { var bytes = attr.Value.ToByteArray(); - c_api.TF_SetAttrValueProto(op_desc, attr.Key, bytes, proto_len: bytes.Length, status: status.Handle); + c_api.TF_SetAttrValueProto(op_desc, attr.Key, bytes, proto_len: bytes.Length, status: status); status.Check(true); } diff --git a/src/TensorFlowNet.Benchmarks/Leak/SavedModelCleanup.cs b/src/TensorFlowNet.Benchmarks/Leak/SavedModelCleanup.cs index 09e20058..9231f3a8 100644 --- a/src/TensorFlowNet.Benchmarks/Leak/SavedModelCleanup.cs +++ b/src/TensorFlowNet.Benchmarks/Leak/SavedModelCleanup.cs @@ -23,16 +23,14 @@ namespace Tensorflow.Benchmark.Leak var ClassifierModelPath = Path.Combine(modelDir, "Leak", "TestModel", "saved_model"); for (var i = 0; i < 1024; i++) - { - using (var sess = Session.LoadFromSavedModel(ClassifierModelPath)) { - using (var g = sess.graph.as_default()) { - var inputOp = g.OperationByName("inference_input"); - var outputOp = g.OperationByName("StatefulPartitionedCall"); + { + var sess = Session.LoadFromSavedModel(ClassifierModelPath); + var g = sess.graph.as_default(); + var inputOp = g.OperationByName("inference_input"); + var outputOp = g.OperationByName("StatefulPartitionedCall"); - var inp = np.zeros(new Shape(new int[] { 1, 2, 96 }), TF_DataType.TF_FLOAT); - sess.run(outputOp.outputs[0], new FeedItem(inputOp.outputs[0], inp)); - } - } + var inp = np.zeros(new Shape(new int[] { 1, 2, 96 }), TF_DataType.TF_FLOAT); + sess.run(outputOp.outputs[0], new FeedItem(inputOp.outputs[0], inp)); } } } diff --git a/test/TensorFlowNET.Graph.UnitTest/Basics/QueueTest.cs b/test/TensorFlowNET.Graph.UnitTest/Basics/QueueTest.cs index f0a4ea84..21c5fdbf 100644 --- a/test/TensorFlowNET.Graph.UnitTest/Basics/QueueTest.cs +++ b/test/TensorFlowNET.Graph.UnitTest/Basics/QueueTest.cs @@ -16,18 +16,16 @@ namespace TensorFlowNET.UnitTest.Basics var enqueue = queue.enqueue(numbers); var dequeue_many = queue.dequeue_many(n: 3); - using (var sess = tf.Session()) - { - sess.run(enqueue, (numbers, new[] { 1 })); - sess.run(enqueue, (numbers, new[] { 2, 3 })); - sess.run(enqueue, (numbers, new[] { 3, 4, 5 })); - - var result = sess.run(dequeue_many[0]); - - Assert.IsTrue(Enumerable.SequenceEqual(new int[] { 1, 0, 0 }, result[0].ToArray())); - Assert.IsTrue(Enumerable.SequenceEqual(new int[] { 2, 3, 0 }, result[1].ToArray())); - Assert.IsTrue(Enumerable.SequenceEqual(new int[] { 3, 4, 5 }, result[2].ToArray())); - } + var sess = tf.Session(); + sess.run(enqueue, (numbers, new[] { 1 })); + sess.run(enqueue, (numbers, new[] { 2, 3 })); + sess.run(enqueue, (numbers, new[] { 3, 4, 5 })); + + var result = sess.run(dequeue_many[0]); + + Assert.IsTrue(Enumerable.SequenceEqual(new int[] { 1, 0, 0 }, result[0].ToArray())); + Assert.IsTrue(Enumerable.SequenceEqual(new int[] { 2, 3, 0 }, result[1].ToArray())); + Assert.IsTrue(Enumerable.SequenceEqual(new int[] { 3, 4, 5 }, result[2].ToArray())); } [TestMethod] @@ -45,27 +43,25 @@ namespace TensorFlowNET.UnitTest.Basics // push back into queue var inc = queue.enqueue(y); - using (var sess = tf.Session()) - { - // init queue - init.run(); + var sess = tf.Session(); + // init queue + init.run(); - // pop out first element and push back calculated y - (int dequeued, _) = sess.run((x, inc)); - Assert.AreEqual(10, dequeued); + // pop out first element and push back calculated y + (int dequeued, _) = sess.run((x, inc)); + Assert.AreEqual(10, dequeued); - (dequeued, _) = sess.run((x, inc)); - Assert.AreEqual(20, dequeued); + (dequeued, _) = sess.run((x, inc)); + Assert.AreEqual(20, dequeued); - (dequeued, _) = sess.run((x, inc)); - Assert.AreEqual(11, dequeued); + (dequeued, _) = sess.run((x, inc)); + Assert.AreEqual(11, dequeued); - (dequeued, _) = sess.run((x, inc)); - Assert.AreEqual(21, dequeued); + (dequeued, _) = sess.run((x, inc)); + Assert.AreEqual(21, dequeued); - // thread will hang or block if you run sess.run(x) again - // until queue has more element. - } + // thread will hang or block if you run sess.run(x) again + // until queue has more element. } [TestMethod] @@ -75,19 +71,17 @@ namespace TensorFlowNET.UnitTest.Basics var init = queue.enqueue_many(new[] { 2L, 4L, 3L }, new[] { "p1", "p2", "p3" }); var x = queue.dequeue(); - using (var sess = tf.Session()) - { - init.run(); + var sess = tf.Session(); + init.run(); - var result = sess.run(x); - Assert.AreEqual(result[0], 2L); + var result = sess.run(x); + Assert.AreEqual(result[0], 2L); - result = sess.run(x); - Assert.AreEqual(result[0], 3L); + result = sess.run(x); + Assert.AreEqual(result[0], 3L); - result = sess.run(x); - Assert.AreEqual(result[0], 4L); - } + result = sess.run(x); + Assert.AreEqual(result[0], 4L); } [TestMethod] @@ -98,16 +92,14 @@ namespace TensorFlowNET.UnitTest.Basics var x = queue.dequeue(); string results = ""; - using (var sess = tf.Session()) - { - init.run(); + var sess = tf.Session(); + init.run(); - foreach (var i in range(9)) - results += (int)sess.run(x) + "."; + foreach (var i in range(9)) + results += (int)sess.run(x) + "."; - // output in random order - Assert.IsFalse(results == "1.2.3.4.5.6.7.8.9."); - } + // output in random order + Assert.IsFalse(results == "1.2.3.4.5.6.7.8.9."); } } } diff --git a/test/TensorFlowNET.Graph.UnitTest/Basics/SessionTest.cs b/test/TensorFlowNET.Graph.UnitTest/Basics/SessionTest.cs index 82380984..2300b094 100644 --- a/test/TensorFlowNET.Graph.UnitTest/Basics/SessionTest.cs +++ b/test/TensorFlowNET.Graph.UnitTest/Basics/SessionTest.cs @@ -19,11 +19,9 @@ namespace TensorFlowNET.UnitTest.Basics var a = constant_op.constant(np.array(3.0).reshape((1, 1))); var b = constant_op.constant(np.array(2.0).reshape((1, 1))); var c = math_ops.matmul(a, b, name: "matmul"); - using (var sess = tf.Session()) - { - var result = c.eval(sess); - Assert.AreEqual(result[0], 6.0); - } + var sess = tf.Session(); + var result = c.eval(sess); + Assert.AreEqual(result[0], 6.0); } } @@ -32,11 +30,9 @@ namespace TensorFlowNET.UnitTest.Basics { var a = constant_op.constant("123 heythere 123 ", TF_DataType.TF_STRING); var c = tf.strings.substr(a, 4, 8); - using (var sess = tf.Session()) - { - var result = c.eval(sess).StringData(); - Assert.AreEqual(result[0], "heythere"); - } + var sess = tf.Session(); + var result = c.eval(sess).StringData(); + Assert.AreEqual(result[0], "heythere"); } [TestMethod] @@ -47,11 +43,9 @@ namespace TensorFlowNET.UnitTest.Basics const int size = 30_000; var a = constant_op.constant(new string('a', size), TF_DataType.TF_STRING); var c = tf.strings.substr(a, 0, size - 5000); - using (var sess = tf.Session()) - { - var result = UTF8Encoding.UTF8.GetString(c.eval(sess).ToByteArray()); - Console.WriteLine(result); - } + var sess = tf.Session(); + var result = UTF8Encoding.UTF8.GetString(c.eval(sess).ToByteArray()); + Console.WriteLine(result); } } diff --git a/test/TensorFlowNET.Graph.UnitTest/Basics/TensorTest.cs b/test/TensorFlowNET.Graph.UnitTest/Basics/TensorTest.cs index 46fe69d3..90de7874 100644 --- a/test/TensorFlowNET.Graph.UnitTest/Basics/TensorTest.cs +++ b/test/TensorFlowNET.Graph.UnitTest/Basics/TensorTest.cs @@ -16,15 +16,13 @@ namespace TensorFlowNET.UnitTest.Basics var labels = tf.expand_dims(tf.constant(new[] { 0, 1, 2, 3, 4 }), 1); var st = tf.concat(values: new[] { indices, labels }, axis: 1); var onehot = tf.sparse_to_dense(st, (5, 5), 1); - using (var sess = tf.Session()) - { - var result = sess.run(onehot); - Assert.IsTrue(Enumerable.SequenceEqual(new int[] { 1, 0, 0, 0, 0 }, result[0].ToArray())); - Assert.IsTrue(Enumerable.SequenceEqual(new int[] { 0, 1, 0, 0, 0 }, result[1].ToArray())); - Assert.IsTrue(Enumerable.SequenceEqual(new int[] { 0, 0, 1, 0, 0 }, result[2].ToArray())); - Assert.IsTrue(Enumerable.SequenceEqual(new int[] { 0, 0, 0, 1, 0 }, result[3].ToArray())); - Assert.IsTrue(Enumerable.SequenceEqual(new int[] { 0, 0, 0, 0, 1 }, result[4].ToArray())); - }; + var sess = tf.Session(); + var result = sess.run(onehot); + Assert.IsTrue(Enumerable.SequenceEqual(new int[] { 1, 0, 0, 0, 0 }, result[0].ToArray())); + Assert.IsTrue(Enumerable.SequenceEqual(new int[] { 0, 1, 0, 0, 0 }, result[1].ToArray())); + Assert.IsTrue(Enumerable.SequenceEqual(new int[] { 0, 0, 1, 0, 0 }, result[2].ToArray())); + Assert.IsTrue(Enumerable.SequenceEqual(new int[] { 0, 0, 0, 1, 0 }, result[3].ToArray())); + Assert.IsTrue(Enumerable.SequenceEqual(new int[] { 0, 0, 0, 0, 1 }, result[4].ToArray())); } [TestMethod, Ignore] @@ -39,13 +37,11 @@ namespace TensorFlowNET.UnitTest.Basics new[] { 3L, 4L }); var onehot = tf.sparse_tensor_to_dense(decoded_list); - using (var sess = tf.Session()) - { - var result = sess.run(onehot); - Assert.IsTrue(Enumerable.SequenceEqual(new int[] { 1, 0, 0, 0 }, result[0].ToArray())); - Assert.IsTrue(Enumerable.SequenceEqual(new int[] { 0, 0, 2, 0 }, result[1].ToArray())); - Assert.IsTrue(Enumerable.SequenceEqual(new int[] { 0, 0, 0, 0 }, result[2].ToArray())); - } + var sess = tf.Session(); + var result = sess.run(onehot); + Assert.IsTrue(Enumerable.SequenceEqual(new int[] { 1, 0, 0, 0 }, result[0].ToArray())); + Assert.IsTrue(Enumerable.SequenceEqual(new int[] { 0, 0, 2, 0 }, result[1].ToArray())); + Assert.IsTrue(Enumerable.SequenceEqual(new int[] { 0, 0, 0, 0 }, result[2].ToArray())); } [TestMethod] @@ -56,14 +52,12 @@ namespace TensorFlowNET.UnitTest.Basics int[,] crops = { { 0, 0 }, { 0, 0 } }; var tensor = tf.batch_to_space_nd(inputs, block_shape, crops); - using (var sess = tf.Session()) - { - var result = sess.run(tensor); - Assert.IsTrue(Enumerable.SequenceEqual(new int[] { 0, 6, 1, 7, 2, 8 }, result[0, 0].ToArray())); - Assert.IsTrue(Enumerable.SequenceEqual(new int[] { 12, 18, 13, 19, 14, 20 }, result[0, 1].ToArray())); - Assert.IsTrue(Enumerable.SequenceEqual(new int[] { 3, 9, 4, 10, 5, 11 }, result[0, 2].ToArray())); - Assert.IsTrue(Enumerable.SequenceEqual(new int[] { 15, 21, 16, 22, 17, 23 }, result[0, 3].ToArray())); - } + var sess = tf.Session(); + var result = sess.run(tensor); + Assert.IsTrue(Enumerable.SequenceEqual(new int[] { 0, 6, 1, 7, 2, 8 }, result[0, 0].ToArray())); + Assert.IsTrue(Enumerable.SequenceEqual(new int[] { 12, 18, 13, 19, 14, 20 }, result[0, 1].ToArray())); + Assert.IsTrue(Enumerable.SequenceEqual(new int[] { 3, 9, 4, 10, 5, 11 }, result[0, 2].ToArray())); + Assert.IsTrue(Enumerable.SequenceEqual(new int[] { 15, 21, 16, 22, 17, 23 }, result[0, 3].ToArray())); } [TestMethod, Ignore] @@ -72,11 +66,9 @@ namespace TensorFlowNET.UnitTest.Basics var tensor = new[] { 0, 1, 2, 3 }; var mask = np.array(new[] { true, false, true, false }); var masked = tf.boolean_mask(tensor, mask); - using (var sess = tf.Session()) - { - var result = sess.run(masked); - Assert.IsTrue(Enumerable.SequenceEqual(new int[] { 0, 2 }, masked.ToArray())); - } + var sess = tf.Session(); + var result = sess.run(masked); + Assert.IsTrue(Enumerable.SequenceEqual(new int[] { 0, 2 }, masked.ToArray())); } } } \ No newline at end of file diff --git a/test/TensorFlowNET.Graph.UnitTest/Basics/VariableTest.cs b/test/TensorFlowNET.Graph.UnitTest/Basics/VariableTest.cs index 35525a1a..3c95501d 100644 --- a/test/TensorFlowNET.Graph.UnitTest/Basics/VariableTest.cs +++ b/test/TensorFlowNET.Graph.UnitTest/Basics/VariableTest.cs @@ -14,7 +14,7 @@ namespace TensorFlowNET.UnitTest.Basics var v = tf.Variable(new[] { 1, 2 }); var init = tf.compat.v1.global_variables_initializer(); - using var sess = tf.compat.v1.Session(); + var sess = tf.compat.v1.Session(); sess.run(init); // Usage passing the session explicitly. print(v.eval(sess)); diff --git a/test/TensorFlowNET.Graph.UnitTest/ControlFlowTest/CondTestCases.cs b/test/TensorFlowNET.Graph.UnitTest/ControlFlowTest/CondTestCases.cs index 917280e4..7063c22c 100644 --- a/test/TensorFlowNET.Graph.UnitTest/ControlFlowTest/CondTestCases.cs +++ b/test/TensorFlowNET.Graph.UnitTest/ControlFlowTest/CondTestCases.cs @@ -16,18 +16,16 @@ namespace TensorFlowNET.UnitTest.ControlFlowTest { var graph = tf.Graph().as_default(); - using (var sess = tf.Session(graph)) - { - var x = tf.constant(2, name: "x"); - var y = tf.constant(5, name: "y"); - - var z = control_flow_ops.cond(tf.less(x, y), - () => tf.constant(22, name: "t22"), - () => tf.constant(55, name: "f55")); - - int result = z.eval(sess); - assertEquals(result, 22); - } + var sess = tf.Session(graph); + var x = tf.constant(2, name: "x"); + var y = tf.constant(5, name: "y"); + + var z = control_flow_ops.cond(tf.less(x, y), + () => tf.constant(22, name: "t22"), + () => tf.constant(55, name: "f55")); + + int result = z.eval(sess); + assertEquals(result, 22); } [TestMethod] @@ -35,18 +33,16 @@ namespace TensorFlowNET.UnitTest.ControlFlowTest { var graph = tf.Graph().as_default(); - using (var sess = tf.Session(graph)) - { - var x = tf.constant(2, name: "x"); - var y = tf.constant(1, name: "y"); + var sess = tf.Session(graph); + var x = tf.constant(2, name: "x"); + var y = tf.constant(1, name: "y"); - var z = control_flow_ops.cond(tf.less(x, y), - () => tf.constant(22, name: "t22"), - () => tf.constant(11, name: "f11")); + var z = control_flow_ops.cond(tf.less(x, y), + () => tf.constant(22, name: "t22"), + () => tf.constant(11, name: "f11")); - int result = z.eval(sess); - assertEquals(result, 11); - } + int result = z.eval(sess); + assertEquals(result, 11); } [Ignore("Dependent on UpdateEdge")] diff --git a/test/TensorFlowNET.Graph.UnitTest/ControlFlowTest/WhileContextTestCase.cs b/test/TensorFlowNET.Graph.UnitTest/ControlFlowTest/WhileContextTestCase.cs index 81425358..a31dea7d 100644 --- a/test/TensorFlowNET.Graph.UnitTest/ControlFlowTest/WhileContextTestCase.cs +++ b/test/TensorFlowNET.Graph.UnitTest/ControlFlowTest/WhileContextTestCase.cs @@ -23,21 +23,19 @@ namespace TensorFlowNET.UnitTest.ControlFlowTest private void _testWhileContextHelper(int maximum_iterations) { // TODO: implement missing code dependencies - using (var sess = this.cached_session()) + var sess = this.cached_session(); + var i = constant_op.constant(0, name: "i"); + var c = new Func(x => gen_math_ops.less(x, 10, name: "c")); + var b = new Func(x => gen_math_ops.add(x, 1, name: "c")); + //control_flow_ops.while_loop( + // c, b, i , maximum_iterations: tf.constant(maximum_iterations)); + foreach (Operation op in sess.graph.get_operations()) { - var i = constant_op.constant(0, name: "i"); - var c = new Func(x => gen_math_ops.less(x, 10, name: "c")); - var b = new Func(x => gen_math_ops.add(x, 1, name: "c")); - //control_flow_ops.while_loop( - // c, b, i , maximum_iterations: tf.constant(maximum_iterations)); - foreach (Operation op in sess.graph.get_operations()) - { - var control_flow_context = op._get_control_flow_context(); - /*if (control_flow_context != null) - self.assertProtoEquals(control_flow_context.to_proto(), - WhileContext.from_proto( - control_flow_context.to_proto()).to_proto(), "");*/ - } + var control_flow_context = op._get_control_flow_context(); + /*if (control_flow_context != null) + self.assertProtoEquals(control_flow_context.to_proto(), + WhileContext.from_proto( + control_flow_context.to_proto()).to_proto(), "");*/ } } diff --git a/test/TensorFlowNET.Graph.UnitTest/GradientTest/GradientTest.cs b/test/TensorFlowNET.Graph.UnitTest/GradientTest/GradientTest.cs index f60fe6d9..92afd6a3 100644 --- a/test/TensorFlowNET.Graph.UnitTest/GradientTest/GradientTest.cs +++ b/test/TensorFlowNET.Graph.UnitTest/GradientTest/GradientTest.cs @@ -18,11 +18,9 @@ namespace TensorFlowNET.UnitTest.Gradient var y = tf.broadcast_to(x, (2, 4, 3)); var grad = tf.gradients(y, x); - using (var sess = tf.Session(graph)) - { - float result = sess.run(grad[0]); - Assert.AreEqual(result, 24.0f); - } + var sess = tf.Session(graph); + float result = sess.run(grad[0]); + Assert.AreEqual(result, 24.0f); } [TestMethod] @@ -33,11 +31,9 @@ namespace TensorFlowNET.UnitTest.Gradient var z = tf.cumsum(y, axis: 1); var grad = tf.gradients(z, x); - using (var sess = tf.Session(graph)) - { - float result = sess.run(grad[0]); - Assert.AreEqual(result, 60.0f); - } + var sess = tf.Session(graph); + float result = sess.run(grad[0]); + Assert.AreEqual(result, 60.0f); } [TestMethod, Ignore] @@ -78,14 +74,12 @@ namespace TensorFlowNET.UnitTest.Gradient 42.0f, 42.0f, 42.0f, 45.0f, 45.0f, 45.0f }; - using (var sess = tf.Session()) - { - var result = sess.run(g); - var resultList = result[0].ToArray().ToList(); - resultList.AddRange(result[1].ToArray()); - Console.WriteLine(result.ToString()); - CollectionAssert.AreEqual(resultList.ToArray(), checkG); - } + var sess = tf.Session(); + var result = sess.run(g); + var resultList = result[0].ToArray().ToList(); + resultList.AddRange(result[1].ToArray()); + Console.WriteLine(result.ToString()); + CollectionAssert.AreEqual(resultList.ToArray(), checkG); } [TestMethod] @@ -97,11 +91,9 @@ namespace TensorFlowNET.UnitTest.Gradient var y = f(x); var g = tf.gradients(y, x); - using (var session = tf.Session()) - { - var result = session.run(new[] { y, g[0] }); - return (result[0].ToArray()[0], result[1].ToArray()[0]); - } + var session = tf.Session(); + var result = session.run(new[] { y, g[0] }); + return (result[0].ToArray()[0], result[1].ToArray()[0]); } void test(string name, Func tfF, Func targetF, double[] values) @@ -197,13 +189,11 @@ namespace TensorFlowNET.UnitTest.Gradient var g1 = tf.gradients(tf.reduce_sum(m, axis: 0)[0], x)[0]; var g2 = tf.gradients(tf.reduce_sum(m, axis: 1)[0], x)[0]; - using (var session = tf.Session()) - { - var (r0, r1, r2) = session.run((g0, g1, g2), new FeedItem(x, new[,] { { 1.0 } })); - self.assertFloat64Equal(6.0, r0[0], $"tf.reduce_sum(...)"); - self.assertFloat64Equal(2.0, r1[0], $"tf.reduce_sum(..., axis = 0)"); - self.assertFloat64Equal(3.0, r2[0], $"tf.reduce_sum(..., axis = 1)"); - } + var session = tf.Session(); + var (r0, r1, r2) = session.run((g0, g1, g2), new FeedItem(x, new[,] { { 1.0 } })); + self.assertFloat64Equal(6.0, r0[0], $"tf.reduce_sum(...)"); + self.assertFloat64Equal(2.0, r1[0], $"tf.reduce_sum(..., axis = 0)"); + self.assertFloat64Equal(3.0, r2[0], $"tf.reduce_sum(..., axis = 1)"); } [TestMethod] @@ -212,12 +202,10 @@ namespace TensorFlowNET.UnitTest.Gradient var a = tf.constant(1f); var b = tf.tanh(a); var g = tf.gradients(b, a); - using (var sess = tf.Session()) - { - var result = sess.run(g); - var actual = result[0]; - Assert.AreEqual(actual, 0.41997434127f); - } + var sess = tf.Session(); + var result = sess.run(g); + var actual = result[0]; + Assert.AreEqual(actual, 0.41997434127f); } @@ -227,14 +215,12 @@ namespace TensorFlowNET.UnitTest.Gradient var a = tf.constant(5f); var b = tf.lgamma(a); var g = tf.gradients(b, a); - using (var sess = tf.Session()) - { - var result = sess.run(new object[] { g, b }); - var actualDeriv = result[0]; - var actual = result[1]; - Assert.AreEqual(actualDeriv, 1.5061177f); - Assert.AreEqual(actual, 3.17805386f); - } + var sess = tf.Session(); + var result = sess.run(new object[] { g, b }); + var actualDeriv = result[0]; + var actual = result[1]; + Assert.AreEqual(actualDeriv, 1.5061177f); + Assert.AreEqual(actual, 3.17805386f); } [TestMethod] @@ -247,14 +233,12 @@ namespace TensorFlowNET.UnitTest.Gradient tf.constant(new[] { 1 }, tf.int32, new[] { 1 }) ); var g = tf.gradients(b, a); - using (var sess = tf.Session()) - { - var result = sess.run(new object[] { g, b }); - var actualDeriv = np.squeeze(result[0]); - var actual = np.squeeze(result[1]); - Assert.AreEqual(actualDeriv, new float[] { 1, 0 }); - Assert.AreEqual(actual, 0.9640276f); - } + var sess = tf.Session(); + var result = sess.run(new object[] { g, b }); + var actualDeriv = np.squeeze(result[0]); + var actual = np.squeeze(result[1]); + Assert.AreEqual(actualDeriv, new float[] { 1, 0 }); + Assert.AreEqual(actual, 0.9640276f); } [TestMethod] @@ -264,14 +248,12 @@ namespace TensorFlowNET.UnitTest.Gradient var a2 = tf.constant(new[] { 3f }, shape: new[] { 1 }); var a = tf.concat(new List(new[] { a1, a2 }), 0); var g = tf.gradients(a, a1); - using (var sess = tf.Session()) - { - var result = sess.run(new object[] { g, a }); - var actualDeriv = result[0][0]; - var actual = result[1][0]; - Assert.AreEqual(actualDeriv, 1f); - Assert.AreEqual(actual, 2f); - } + var sess = tf.Session(); + var result = sess.run(new object[] { g, a }); + var actualDeriv = result[0][0]; + var actual = result[1][0]; + Assert.AreEqual(actualDeriv, 1f); + Assert.AreEqual(actual, 2f); } [TestMethod] @@ -280,13 +262,12 @@ namespace TensorFlowNET.UnitTest.Gradient var ap = tf.constant(1f); var b = tf.tanh(ap) + gen_array_ops.stop_gradient(ap); var g = tf.gradients(b, ap); - using (var sess = tf.Session()) - { - var result = sess.run(g); - var actual = result[0]; - Assert.AreEqual(actual, 0.41997434127f); - } + var sess = tf.Session(); + var result = sess.run(g); + var actual = result[0]; + Assert.AreEqual(actual, 0.41997434127f); } + [Ignore("TODO")] [TestMethod] public void testUnusedOutput() diff --git a/test/TensorFlowNET.Graph.UnitTest/ImageTest.cs b/test/TensorFlowNET.Graph.UnitTest/ImageTest.cs index 29ad9ad8..c42445cf 100644 --- a/test/TensorFlowNET.Graph.UnitTest/ImageTest.cs +++ b/test/TensorFlowNET.Graph.UnitTest/ImageTest.cs @@ -74,23 +74,21 @@ namespace TensorFlowNET.UnitTest var cropSize2_2 = tf.Variable(np.array(4, 4)); var init = tf.global_variables_initializer(); - using (Session sess = tf.Session()) - { - sess.run(init); + var sess = tf.Session(); + sess.run(init); - var cropped = tf.image.crop_and_resize(image, box, boxInd, cropSize1_1); + var cropped = tf.image.crop_and_resize(image, box, boxInd, cropSize1_1); - var result = sess.run(cropped); - // check if cropped to 1x1 center was succesfull - Assert.AreEqual(result.size, 1ul); - Assert.AreEqual(result[0, 0, 0, 0], 4f); + var result = sess.run(cropped); + // check if cropped to 1x1 center was succesfull + Assert.AreEqual(result.size, 1ul); + Assert.AreEqual(result[0, 0, 0, 0], 4f); - cropped = tf.image.crop_and_resize(image2, box, boxInd, cropSize2_2); - result = sess.run(cropped); - // check if flipped and no cropping occured - Assert.AreEqual(result.size, 16ul); - Assert.AreEqual(result[0, 0, 0, 0], 12f); - } + cropped = tf.image.crop_and_resize(image2, box, boxInd, cropSize2_2); + result = sess.run(cropped); + // check if flipped and no cropping occured + Assert.AreEqual(result.size, 16ul); + Assert.AreEqual(result[0, 0, 0, 0], 12f); } } } diff --git a/test/TensorFlowNET.Graph.UnitTest/MultithreadingTests.cs b/test/TensorFlowNET.Graph.UnitTest/MultithreadingTests.cs index f657acc7..4b92d021 100644 --- a/test/TensorFlowNET.Graph.UnitTest/MultithreadingTests.cs +++ b/test/TensorFlowNET.Graph.UnitTest/MultithreadingTests.cs @@ -24,7 +24,7 @@ namespace TensorFlowNET.UnitTest { Assert.IsNull(tf.peak_default_graph()); - using var sess = tf.Session(); + var sess = tf.Session(); var default_graph = tf.get_default_graph(); var sess_graph = sess.graph; Assert.IsNotNull(default_graph); @@ -45,7 +45,7 @@ namespace TensorFlowNET.UnitTest { Assert.IsNull(tf.peak_default_graph()); //tf.Session created an other graph - using var sess = tf.Session(); + var sess = tf.Session(); var default_graph = tf.get_default_graph(); var sess_graph = sess.graph; Assert.IsNotNull(default_graph); @@ -69,7 +69,7 @@ namespace TensorFlowNET.UnitTest beforehand.as_default(); Assert.IsNotNull(tf.peak_default_graph()); - using var sess = tf.Session(); + var sess = tf.Session(); var default_graph = tf.peak_default_graph(); var sess_graph = sess.graph; Assert.IsNotNull(default_graph); @@ -102,7 +102,7 @@ namespace TensorFlowNET.UnitTest //the core method void Core(int tid) { - using var sess = tf.Session(); + var sess = tf.Session(); for (int i = 0; i < 100; i++) { var t = new Tensor(1); @@ -119,7 +119,7 @@ namespace TensorFlowNET.UnitTest void Core(int tid) { //tf.Session created an other graph - using var sess = tf.Session(); + var sess = tf.Session(); for (int i = 0; i < 100; i++) { var t = new Tensor(new int[] { 1, 2, 3 }); @@ -142,7 +142,7 @@ namespace TensorFlowNET.UnitTest var a1 = tf.constant(new[] { 2f }, shape: new[] { 1 }); var a2 = tf.constant(new[] { 3f }, shape: new[] { 1 }); var math = a1 + a2; - using var sess = tf.Session(graph); + var sess = tf.Session(graph); for (int i = 0; i < 100; i++) { var result = sess.run(math); @@ -162,7 +162,7 @@ namespace TensorFlowNET.UnitTest tf.compat.v1.disable_eager_execution(); var graph = tf.Graph().as_default(); - using var sess = tf.Session(graph); + var sess = tf.Session(graph); Assert.IsNotNull(tf.get_default_graph()); //graph is created automatically to perform create these operations var a1 = tf.constant(new[] { 2f }, shape: new[] { 1 }); @@ -182,7 +182,7 @@ namespace TensorFlowNET.UnitTest //the core method void Core(int tid) { - using var sess = tf.Session(); + var sess = tf.Session(); Assert.IsNotNull(tf.get_default_graph()); //graph is created automatically to perform create these operations var a1 = tf.constant(new[] { 2f }, shape: new[] { 1 }); diff --git a/test/TensorFlowNET.Graph.UnitTest/OperationsTest.cs b/test/TensorFlowNET.Graph.UnitTest/OperationsTest.cs index 89dce0e1..47887e29 100644 --- a/test/TensorFlowNET.Graph.UnitTest/OperationsTest.cs +++ b/test/TensorFlowNET.Graph.UnitTest/OperationsTest.cs @@ -20,7 +20,7 @@ namespace TensorFlowNET.UnitTest.Basics public void GetAllOpList() { var handle = c_api.TF_GetAllOpList(); - using var buffer = new Buffer(handle); + var buffer = new Buffer(handle); var op_list = OpList.Parser.ParseFrom(buffer.ToArray()); var _registered_ops = new Dictionary(); @@ -39,13 +39,11 @@ namespace TensorFlowNET.UnitTest.Basics var b = tf.placeholder(tf.float32); var c = tf.add(a, b); - using (var sess = tf.Session()) - { - var o = sess.run(c, - new FeedItem(a, 3.0f), - new FeedItem(b, 2.0f)); - Assert.AreEqual(o, 5.0f); - } + var sess = tf.Session(); + var o = sess.run(c, + new FeedItem(a, 3.0f), + new FeedItem(b, 2.0f)); + Assert.AreEqual(o, 5.0f); } [TestMethod] @@ -55,11 +53,9 @@ namespace TensorFlowNET.UnitTest.Basics var b = tf.constant(5.0f); var c = tf.add(a, b); - using (var sess = tf.Session()) - { - var o = sess.run(c); - Assert.AreEqual(o, 9.0f); - } + var sess = tf.Session(); + var o = sess.run(c); + Assert.AreEqual(o, 9.0f); } [TestMethod] @@ -69,11 +65,9 @@ namespace TensorFlowNET.UnitTest.Basics var b = tf.cast(tf.is_finite(a), tf.float32); var check = np.array(1.0f, 0.0f, 1.0f, 0.0f, 1.0f, 0.0f, 1.0f, 0.0f); - using (var sess = tf.Session()) - { - var o = sess.run(b); - Assert.IsTrue(np.array_equal(o, check)); - } + var sess = tf.Session(); + var o = sess.run(b); + Assert.IsTrue(np.array_equal(o, check)); } [TestMethod] @@ -83,11 +77,9 @@ namespace TensorFlowNET.UnitTest.Basics var b = tf.cast(tf.is_nan(a), tf.float32); var check = np.array(0.0f, 1.0f, 0.0f, 1.0f, 0.0f, 1.0f, 0.0f, 1.0f); - using (var sess = tf.Session()) - { - var o = sess.run(b); - Assert.IsTrue(np.array_equal(o, check)); - } + var sess = tf.Session(); + var o = sess.run(b); + Assert.IsTrue(np.array_equal(o, check)); } [TestMethod] @@ -97,38 +89,30 @@ namespace TensorFlowNET.UnitTest.Basics var b = tf.cumsum(a); var check = np.array(1, 2, 4, 7, 11, 16); - using (var sess = tf.Session()) - { - var o = sess.run(b); - Assert.IsTrue(np.array_equal(o, check)); - } + var sess = tf.Session(); + var o = sess.run(b); + Assert.IsTrue(np.array_equal(o, check)); b = tf.cumsum(a, exclusive: true); check = np.array(0, 1, 2, 4, 7, 11); - using (var sess = tf.Session()) - { - var o = sess.run(b); - Assert.IsTrue(np.array_equal(o, check)); - } + sess = tf.Session(); + o = sess.run(b); + Assert.IsTrue(np.array_equal(o, check)); b = tf.cumsum(a, reverse: true); check = np.array(16, 15, 14, 12, 9, 5); - using (var sess = tf.Session()) - { - var o = sess.run(b); - Assert.IsTrue(np.array_equal(o, check)); - } + sess = tf.Session(); + o = sess.run(b); + Assert.IsTrue(np.array_equal(o, check)); b = tf.cumsum(a, exclusive: true, reverse: true); check = np.array(15, 14, 12, 9, 5, 0); - using (var sess = tf.Session()) - { - var o = sess.run(b); - Assert.IsTrue(np.array_equal(o, check)); - } + sess = tf.Session(); + o = sess.run(b); + Assert.IsTrue(np.array_equal(o, check)); } [TestMethod] @@ -140,38 +124,30 @@ namespace TensorFlowNET.UnitTest.Basics var d = tf.cast(tf.logical_and(b, c), tf.int32); var check = np.array(new[] { 0, 0, 0, 0, 0, 0, 0, 0 }); - using (var sess = tf.Session()) - { - var o = sess.run(d); - Assert.IsTrue(np.array_equal(o, check)); - } + var sess = tf.Session(); + var o = sess.run(d); + Assert.IsTrue(np.array_equal(o, check)); d = tf.cast(tf.logical_not(b), tf.int32); check = np.array(new[] { 1, 1, 1, 1, 0, 0, 0, 0 }); - using (var sess = tf.Session()) - { - var o = sess.run(d); - Assert.IsTrue(np.array_equal(o, check)); - } + sess = tf.Session(); + o = sess.run(d); + Assert.IsTrue(np.array_equal(o, check)); d = tf.cast(tf.logical_or(b, c), tf.int32); check = np.array(new[] { 1, 1, 1, 1, 1, 1, 1, 1 }); - using (var sess = tf.Session()) - { - var o = sess.run(d); - Assert.IsTrue(np.array_equal(o, check)); - } + sess = tf.Session(); + o = sess.run(d); + Assert.IsTrue(np.array_equal(o, check)); d = tf.cast(tf.logical_xor(b, c), tf.int32); check = np.array(new[] { 1, 1, 1, 1, 1, 1, 1, 1 }); - using (var sess = tf.Session()) - { - var o = sess.run(d); - Assert.IsTrue(np.array_equal(o, check)); - } + sess = tf.Session(); + o = sess.run(d); + Assert.IsTrue(np.array_equal(o, check)); } [TestMethod] @@ -192,41 +168,33 @@ namespace TensorFlowNET.UnitTest.Basics var b = tf.placeholder(tf.int32, shape: new Shape(rows, cols)); var c = tf.reduce_sum(tf.reduce_sum(tf.add(a, b), 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, - new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols))), - new FeedItem(b, new NDArray(secondIntFeed, new Shape(rows, cols)))); - Assert.AreEqual(o, intResult); - } + var sess = tf.Session(); + var o = sess.run(c, + new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols))), + new FeedItem(b, new NDArray(secondIntFeed, new Shape(rows, cols)))); + Assert.AreEqual(o, intResult); // Testing `operator +(Tensor x, Tensor y)` c = tf.reduce_sum(tf.reduce_sum(a + b, 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, - new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols))), - new FeedItem(b, new NDArray(secondIntFeed, new Shape(rows, cols)))); - Assert.AreEqual(o, intResult); - } + sess = tf.Session(); + o = sess.run(c, + new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols))), + new FeedItem(b, new NDArray(secondIntFeed, new Shape(rows, cols)))); + Assert.AreEqual(o, intResult); // Testing `operator +(Tensor x, int y)` c = tf.reduce_sum(tf.reduce_sum(a + secondIntVal, 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, - new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols)))); - Assert.AreEqual(o, intResult); - } + sess = tf.Session(); + o = sess.run(c, + new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols)))); + Assert.AreEqual(o, intResult); // Testing `operator +(int x, Tensor y)` c = tf.reduce_sum(tf.reduce_sum(secondIntVal + a, 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, - new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols)))); - Assert.AreEqual(o, intResult); - } + sess = tf.Session(); + o = sess.run(c, + new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols)))); + Assert.AreEqual(o, intResult); #endregion #region floatTest @@ -241,41 +209,33 @@ namespace TensorFlowNET.UnitTest.Basics b = tf.placeholder(tf.float32, shape: new Shape(rows, cols)); c = tf.reduce_sum(tf.reduce_sum(tf.add(a, b), 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, - new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols))), - new FeedItem(b, new NDArray(secondFloatFeed, new Shape(rows, cols)))); - Assert.AreEqual(o, floatResult); - } + sess = tf.Session(); + o = sess.run(c, + new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols))), + new FeedItem(b, new NDArray(secondFloatFeed, new Shape(rows, cols)))); + Assert.AreEqual(o, floatResult); // Testing `operator +(Tensor x, Tensor y) c = tf.reduce_sum(tf.reduce_sum(a + b, 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, - new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols))), - new FeedItem(b, new NDArray(secondFloatFeed, new Shape(rows, cols)))); - Assert.AreEqual(o, floatResult); - } + sess = tf.Session(); + o = sess.run(c, + new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols))), + new FeedItem(b, new NDArray(secondFloatFeed, new Shape(rows, cols)))); + Assert.AreEqual(o, floatResult); // Testing `operator +(Tensor x, float y) c = tf.reduce_sum(tf.reduce_sum(a + secondFloatVal, 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, - new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols)))); - Assert.AreEqual(o, floatResult); - } + sess = tf.Session(); + o = sess.run(c, + new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols)))); + Assert.AreEqual(o, floatResult); // Testing `operator +(float x, Tensor y) c = tf.reduce_sum(tf.reduce_sum(secondFloatVal + a, 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, - new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols)))); - Assert.AreEqual(o, floatResult); - } + sess = tf.Session(); + o = sess.run(c, + new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols)))); + Assert.AreEqual(o, floatResult); #endregion #region doubleTest @@ -290,41 +250,33 @@ namespace TensorFlowNET.UnitTest.Basics b = tf.placeholder(tf.float64, shape: new Shape(rows, cols)); c = tf.reduce_sum(tf.reduce_sum(tf.add(a, b), 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, - new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols))), - new FeedItem(b, new NDArray(secondDoubleFeed, new Shape(rows, cols)))); - Assert.AreEqual((double)o, doubleResult); - } + sess = tf.Session(); + o = sess.run(c, + new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols))), + new FeedItem(b, new NDArray(secondDoubleFeed, new Shape(rows, cols)))); + Assert.AreEqual((double)o, doubleResult); // Testing `operator +(Tensor x, Tensor y) c = tf.reduce_sum(tf.reduce_sum(a + b, 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, - new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols))), - new FeedItem(b, new NDArray(secondDoubleFeed, new Shape(rows, cols)))); - Assert.AreEqual(o, doubleResult); - } + sess = tf.Session(); + o = sess.run(c, + new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols))), + new FeedItem(b, new NDArray(secondDoubleFeed, new Shape(rows, cols)))); + Assert.AreEqual(o, doubleResult); // Testing `operator +(Tensor x, double y) c = tf.reduce_sum(tf.reduce_sum(a + secondDoubleVal, 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, - new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols)))); - Assert.AreEqual(o, doubleResult); - } + sess = tf.Session(); + o = sess.run(c, + new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols)))); + Assert.AreEqual(o, doubleResult); // Testing `operator +(double x, Tensor y) c = tf.reduce_sum(tf.reduce_sum(secondDoubleVal + a, 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, - new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols)))); - Assert.AreEqual(o, doubleResult); - } + sess = tf.Session(); + o = sess.run(c, + new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols)))); + Assert.AreEqual(o, doubleResult); #endregion } @@ -347,50 +299,40 @@ namespace TensorFlowNET.UnitTest.Basics var b = tf.placeholder(tf.int32, shape: new Shape(rows, cols)); var c = tf.reduce_sum(tf.reduce_sum(tf.sub(a, b), 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, - new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols))), - new FeedItem(b, new NDArray(secondIntFeed, new Shape(rows, cols)))); - Assert.AreEqual((int)o, intResult); - } + var sess = tf.Session(); + var o = sess.run(c, + new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols))), + new FeedItem(b, new NDArray(secondIntFeed, new Shape(rows, cols)))); + Assert.AreEqual((int)o, intResult); // Testing `operator -(Tensor x, Tensor y) c = tf.reduce_sum(tf.reduce_sum(a - b, 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, - new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols))), - new FeedItem(b, new NDArray(secondIntFeed, new Shape(rows, cols)))); - Assert.AreEqual((int)o, intResult); - } + sess = tf.Session(); + o = sess.run(c, + new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols))), + new FeedItem(b, new NDArray(secondIntFeed, new Shape(rows, cols)))); + Assert.AreEqual((int)o, intResult); // Testing `operator -(Tensor x, int y) c = tf.reduce_sum(tf.reduce_sum(a - secondIntVal, 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, - new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols)))); - Assert.AreEqual((int)o, intResult); - } + sess = tf.Session(); + o = sess.run(c, + new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols)))); + Assert.AreEqual((int)o, intResult); // Testing `operator -(int x, Tensor y) c = tf.reduce_sum(tf.reduce_sum(secondIntVal - a, 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, - new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols)))); - Assert.AreEqual((int)o, Math.Abs(intResult)); - } + sess = tf.Session(); + o = sess.run(c, + new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols)))); + Assert.AreEqual((int)o, Math.Abs(intResult)); // Testing `operator -(Tensor x) c = tf.reduce_sum(tf.reduce_sum(-a, 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, - new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols)))); - Assert.AreEqual((int)o, intResultTwo); - } + sess = tf.Session(); + o = sess.run(c, + new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols)))); + Assert.AreEqual((int)o, intResultTwo); #endregion #region floatTest @@ -406,50 +348,40 @@ namespace TensorFlowNET.UnitTest.Basics b = tf.placeholder(tf.float32, shape: new Shape(rows, cols)); c = tf.reduce_sum(tf.reduce_sum(tf.sub(a, b), 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, - new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols))), - new FeedItem(b, new NDArray(secondFloatFeed, new Shape(rows, cols)))); - Assert.AreEqual((float)o, floatResult); - } + sess = tf.Session(); + o = sess.run(c, + new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols))), + new FeedItem(b, new NDArray(secondFloatFeed, new Shape(rows, cols)))); + Assert.AreEqual((float)o, floatResult); // Testing `operator -(Tensor x, Tensor y) c = tf.reduce_sum(tf.reduce_sum(a - b, 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, - new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols))), - new FeedItem(b, new NDArray(secondFloatFeed, new Shape(rows, cols)))); - Assert.AreEqual((float)o, floatResult); - } + sess = tf.Session(); + o = sess.run(c, + new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols))), + new FeedItem(b, new NDArray(secondFloatFeed, new Shape(rows, cols)))); + Assert.AreEqual((float)o, floatResult); // Testing `operator -(Tensor x, float y) c = tf.reduce_sum(tf.reduce_sum(a - secondFloatVal, 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, - new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols)))); - Assert.AreEqual((float)o, floatResult); - } + sess = tf.Session(); + o = sess.run(c, + new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols)))); + Assert.AreEqual((float)o, floatResult); // Testing `operator -(float x, Tensor y) c = tf.reduce_sum(tf.reduce_sum(secondFloatVal - a, 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, - new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols)))); - Assert.AreEqual((float)o, Math.Abs(floatResult)); - } + sess = tf.Session(); + o = sess.run(c, + new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols)))); + Assert.AreEqual((float)o, Math.Abs(floatResult)); // Testing `operator -(Tensor x) c = tf.reduce_sum(tf.reduce_sum(-a, 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, - new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols)))); - Assert.AreEqual((float)o, floatResultTwo); - } + sess = tf.Session(); + o = sess.run(c, + new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols)))); + Assert.AreEqual((float)o, floatResultTwo); #endregion #region doubleTest @@ -465,50 +397,40 @@ namespace TensorFlowNET.UnitTest.Basics b = tf.placeholder(tf.float64, shape: new Shape(rows, cols)); c = tf.reduce_sum(tf.reduce_sum(tf.sub(a, b), 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, - new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols))), - new FeedItem(b, new NDArray(secondDoubleFeed, new Shape(rows, cols)))); - Assert.AreEqual((double)o, doubleResult); - } + sess = tf.Session(); + o = sess.run(c, + new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols))), + new FeedItem(b, new NDArray(secondDoubleFeed, new Shape(rows, cols)))); + Assert.AreEqual((double)o, doubleResult); // Testing `operator -(Tensor x, Tensor y) c = tf.reduce_sum(tf.reduce_sum(a - b, 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, - new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols))), - new FeedItem(b, new NDArray(secondDoubleFeed, new Shape(rows, cols)))); - Assert.AreEqual((double)o, doubleResult); - } + sess = tf.Session(); + o = sess.run(c, + new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols))), + new FeedItem(b, new NDArray(secondDoubleFeed, new Shape(rows, cols)))); + Assert.AreEqual((double)o, doubleResult); // Testing `operator -(Tensor x, double y) c = tf.reduce_sum(tf.reduce_sum(a - secondDoubleVal, 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, - new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols)))); - Assert.AreEqual((double)o, doubleResult); - } + sess = tf.Session(); + o = sess.run(c, + new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols)))); + Assert.AreEqual((double)o, doubleResult); // Testing `operator -(double x, Tensor y) c = tf.reduce_sum(tf.reduce_sum(secondDoubleVal - a, 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, - new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols)))); - Assert.AreEqual((double)o, Math.Abs(doubleResult)); - } + sess = tf.Session(); + o = sess.run(c, + new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols)))); + Assert.AreEqual((double)o, Math.Abs(doubleResult)); // Testing `operator -(Tensor x) c = tf.reduce_sum(tf.reduce_sum(-a, 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, - new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols)))); - Assert.AreEqual((double)o, doubleResultTwo); - } + sess = tf.Session(); + o = sess.run(c, + new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols)))); + Assert.AreEqual((double)o, doubleResultTwo); #endregion } @@ -588,41 +510,33 @@ namespace TensorFlowNET.UnitTest.Basics var b = tf.placeholder(tf.int32, shape: new Shape(rows, cols)); var c = tf.reduce_sum(tf.reduce_sum(tf.multiply(a, b), 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, - new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols))), - new FeedItem(b, new NDArray(secondIntFeed, new Shape(rows, cols)))); - Assert.AreEqual((int)o, intResult); - } + var sess = tf.Session(); + var o = sess.run(c, + new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols))), + new FeedItem(b, new NDArray(secondIntFeed, new Shape(rows, cols)))); + Assert.AreEqual((int)o, intResult); // Testing `operator *(Tensor x, Tensor y) c = tf.reduce_sum(tf.reduce_sum(a * b, 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, - new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols))), - new FeedItem(b, new NDArray(secondIntFeed, new Shape(rows, cols)))); - Assert.AreEqual((int)o, intResult); - } + sess = tf.Session(); + o = sess.run(c, + new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols))), + new FeedItem(b, new NDArray(secondIntFeed, new Shape(rows, cols)))); + Assert.AreEqual((int)o, intResult); // Testing `operator *(Tensor x, int y) c = tf.reduce_sum(tf.reduce_sum(a * secondIntVal, 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, - new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols)))); - Assert.AreEqual((int)o, intResult); - } + sess = tf.Session(); + o = sess.run(c, + new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols)))); + Assert.AreEqual((int)o, intResult); // Testing `operator *(int x, Tensor y) c = tf.reduce_sum(tf.reduce_sum(firstIntVal * b, 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, - new FeedItem(b, new NDArray(secondIntFeed, new Shape(rows, cols)))); - Assert.AreEqual((int)o, intResult); - } + sess = tf.Session(); + o = sess.run(c, + new FeedItem(b, new NDArray(secondIntFeed, new Shape(rows, cols)))); + Assert.AreEqual((int)o, intResult); #endregion #region floatTest @@ -637,41 +551,33 @@ namespace TensorFlowNET.UnitTest.Basics b = tf.placeholder(tf.float32, shape: new Shape(rows, cols)); c = tf.reduce_sum(tf.reduce_sum(tf.multiply(a, b), 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, - new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols))), - new FeedItem(b, new NDArray(secondFloatFeed, new Shape(rows, cols)))); - Assert.AreEqual((float)o, floatResult); - } + sess = tf.Session(); + o = sess.run(c, + new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols))), + new FeedItem(b, new NDArray(secondFloatFeed, new Shape(rows, cols)))); + Assert.AreEqual((float)o, floatResult); // Testing `operator *(Tensor x, Tensor y) c = tf.reduce_sum(tf.reduce_sum(a * b, 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, - new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols))), - new FeedItem(b, new NDArray(secondFloatFeed, new Shape(rows, cols)))); - Assert.AreEqual((float)o, floatResult); - } + sess = tf.Session(); + o = sess.run(c, + new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols))), + new FeedItem(b, new NDArray(secondFloatFeed, new Shape(rows, cols)))); + Assert.AreEqual((float)o, floatResult); // Testing `operator *(Tensor x, float y) c = tf.reduce_sum(tf.reduce_sum(a * secondFloatVal, 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, - new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols)))); - Assert.AreEqual((float)o, floatResult); - } + sess = tf.Session(); + o = sess.run(c, + new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols)))); + Assert.AreEqual((float)o, floatResult); // Testing `operator *(float x, Tensor y) c = tf.reduce_sum(tf.reduce_sum(firstFloatVal * b, 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, - new FeedItem(b, new NDArray(secondFloatFeed, new Shape(rows, cols)))); - Assert.AreEqual((float)o, floatResult); - } + sess = tf.Session(); + o = sess.run(c, + new FeedItem(b, new NDArray(secondFloatFeed, new Shape(rows, cols)))); + Assert.AreEqual((float)o, floatResult); #endregion #region doubleTest @@ -686,41 +592,33 @@ namespace TensorFlowNET.UnitTest.Basics b = tf.placeholder(tf.float64, shape: new Shape(rows, cols)); c = tf.reduce_sum(tf.reduce_sum(tf.multiply(a, b), 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, - new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols))), - new FeedItem(b, new NDArray(secondDoubleFeed, new Shape(rows, cols)))); - Assert.AreEqual((double)o, doubleResult); - } + sess = tf.Session(); + o = sess.run(c, + new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols))), + new FeedItem(b, new NDArray(secondDoubleFeed, new Shape(rows, cols)))); + Assert.AreEqual((double)o, doubleResult); // Testing `operator *(Tensor x, Tensor y) c = tf.reduce_sum(tf.reduce_sum(a * b, 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, - new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols))), - new FeedItem(b, new NDArray(secondDoubleFeed, new Shape(rows, cols)))); - Assert.AreEqual((double)o, doubleResult); - } + sess = tf.Session(); + o = sess.run(c, + new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols))), + new FeedItem(b, new NDArray(secondDoubleFeed, new Shape(rows, cols)))); + Assert.AreEqual((double)o, doubleResult); // Testing `operator *(Tensor x, double y) c = tf.reduce_sum(tf.reduce_sum(a * secondDoubleVal, 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, - new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols)))); - Assert.AreEqual((double)o, doubleResult); - } + sess = tf.Session(); + o = sess.run(c, + new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols)))); + Assert.AreEqual((double)o, doubleResult); // Testing `operator *(double x, Tensor y) c = tf.reduce_sum(tf.reduce_sum(firstDoubleVal * b, 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, - new FeedItem(b, new NDArray(secondDoubleFeed, new Shape(rows, cols)))); - Assert.AreEqual((double)o, doubleResult); - } + sess = tf.Session(); + o = sess.run(c, + new FeedItem(b, new NDArray(secondDoubleFeed, new Shape(rows, cols)))); + Assert.AreEqual((double)o, doubleResult); #endregion } @@ -743,41 +641,33 @@ namespace TensorFlowNET.UnitTest.Basics var b = tf.placeholder(tf.int32, shape: new Shape(rows, cols)); var c = tf.reduce_sum(tf.reduce_sum(gen_math_ops.floor_div(a, b), 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, - new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols))), - new FeedItem(b, new NDArray(secondIntFeed, new Shape(rows, cols)))); - Assert.AreEqual((int)o, intResult); - } + var sess = tf.Session(); + var o = sess.run(c, + new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols))), + new FeedItem(b, new NDArray(secondIntFeed, new Shape(rows, cols)))); + Assert.AreEqual((int)o, intResult); // Testing `operator /(Tensor x, Tensor y) c = tf.reduce_sum(tf.reduce_sum(a / b, 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, - new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols))), - new FeedItem(b, new NDArray(secondIntFeed, new Shape(rows, cols)))); - Assert.AreEqual((int)o, intResult); - } + sess = tf.Session(); + o = sess.run(c, + new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols))), + new FeedItem(b, new NDArray(secondIntFeed, new Shape(rows, cols)))); + Assert.AreEqual((int)o, intResult); // Testing `operator /(Tensor x, int y) c = tf.reduce_sum(tf.reduce_sum(a / secondIntVal, 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, - new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols)))); - Assert.AreEqual((int)o, intResult); - } + sess = tf.Session(); + o = sess.run(c, + new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols)))); + Assert.AreEqual((int)o, intResult); // Testing `operator /(int x, Tensor y) c = tf.reduce_sum(tf.reduce_sum(firstIntVal / b, 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, - new FeedItem(b, new NDArray(secondIntFeed, new Shape(rows, cols)))); - Assert.AreEqual((int)o, intResult); - } + sess = tf.Session(); + o = sess.run(c, + new FeedItem(b, new NDArray(secondIntFeed, new Shape(rows, cols)))); + Assert.AreEqual((int)o, intResult); #endregion #region floatTest @@ -792,41 +682,33 @@ namespace TensorFlowNET.UnitTest.Basics b = tf.placeholder(tf.float32, shape: new Shape(rows, cols)); c = tf.reduce_sum(tf.reduce_sum(tf.divide(a, b), 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, - new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols))), - new FeedItem(b, new NDArray(secondFloatFeed, new Shape(rows, cols)))); - Assert.AreEqual((float)o, floatResult); - } + sess = tf.Session(); + o = sess.run(c, + new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols))), + new FeedItem(b, new NDArray(secondFloatFeed, new Shape(rows, cols)))); + Assert.AreEqual((float)o, floatResult); // Testing `operator /(Tensor x, Tensor y) c = tf.reduce_sum(tf.reduce_sum(a / b, 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, - new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols))), - new FeedItem(b, new NDArray(secondFloatFeed, new Shape(rows, cols)))); - Assert.AreEqual((float)o, floatResult); - } + sess = tf.Session(); + o = sess.run(c, + new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols))), + new FeedItem(b, new NDArray(secondFloatFeed, new Shape(rows, cols)))); + Assert.AreEqual((float)o, floatResult); // Testing `operator /(Tensor x, float y) c = tf.reduce_sum(tf.reduce_sum(a / secondFloatVal, 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, - new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols)))); - Assert.AreEqual((float)o, floatResult); - } + sess = tf.Session(); + o = sess.run(c, + new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols)))); + Assert.AreEqual((float)o, floatResult); // Testing `operator /(float x, Tensor y) c = tf.reduce_sum(tf.reduce_sum(firstFloatVal / b, 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, - new FeedItem(b, new NDArray(secondFloatFeed, new Shape(rows, cols)))); - Assert.AreEqual((float)o, floatResult); - } + sess = tf.Session(); + o = sess.run(c, + new FeedItem(b, new NDArray(secondFloatFeed, new Shape(rows, cols)))); + Assert.AreEqual((float)o, floatResult); #endregion #region doubleTest @@ -841,41 +723,33 @@ namespace TensorFlowNET.UnitTest.Basics b = tf.placeholder(tf.float64, shape: new Shape(rows, cols)); c = tf.reduce_sum(tf.reduce_sum(tf.divide(a, b), 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, - new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols))), - new FeedItem(b, new NDArray(secondDoubleFeed, new Shape(rows, cols)))); - Assert.AreEqual((double)o, doubleResult); - } + sess = tf.Session(); + o = sess.run(c, + new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols))), + new FeedItem(b, new NDArray(secondDoubleFeed, new Shape(rows, cols)))); + Assert.AreEqual((double)o, doubleResult); // Testing `operator /(Tensor x, Tensor y) c = tf.reduce_sum(tf.reduce_sum(a / b, 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, - new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols))), - new FeedItem(b, new NDArray(secondDoubleFeed, new Shape(rows, cols)))); - Assert.AreEqual((double)o, doubleResult); - } + sess = tf.Session(); + o = sess.run(c, + new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols))), + new FeedItem(b, new NDArray(secondDoubleFeed, new Shape(rows, cols)))); + Assert.AreEqual((double)o, doubleResult); // Testing `operator /(Tensor x, double y) c = tf.reduce_sum(tf.reduce_sum(a / secondFloatVal, 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, - new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols)))); - Assert.AreEqual((double)o, doubleResult); - } + sess = tf.Session(); + o = sess.run(c, + new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols)))); + Assert.AreEqual((double)o, doubleResult); // Testing `operator /(double x, Tensor y) c = tf.reduce_sum(tf.reduce_sum(firstFloatVal / b, 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, - new FeedItem(b, new NDArray(secondDoubleFeed, new Shape(rows, cols)))); - Assert.AreEqual((double)o, doubleResult); - } + sess = tf.Session(); + o = sess.run(c, + new FeedItem(b, new NDArray(secondDoubleFeed, new Shape(rows, cols)))); + Assert.AreEqual((double)o, doubleResult); #endregion } @@ -897,41 +771,33 @@ namespace TensorFlowNET.UnitTest.Basics var b = tf.placeholder(tf.int32, shape: new Shape(rows, cols)); var c = tf.reduce_sum(tf.reduce_sum(tf.cast(tf.greater(a, b), tf.int32), 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, - new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols))), - new FeedItem(b, new NDArray(secondIntFeed, new Shape(rows, cols)))); - Assert.AreEqual((int)o, intResult); - } + var sess = tf.Session(); + var o = sess.run(c, + new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols))), + new FeedItem(b, new NDArray(secondIntFeed, new Shape(rows, cols)))); + Assert.AreEqual((int)o, intResult); // Testing `operator >(Tensor x, Tensor y) c = tf.reduce_sum(tf.reduce_sum(tf.cast(a > b, tf.int32), 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, - new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols))), - new FeedItem(b, new NDArray(secondIntFeed, new Shape(rows, cols)))); - Assert.AreEqual((int)o, intResult); - } + sess = tf.Session(); + o = sess.run(c, + new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols))), + new FeedItem(b, new NDArray(secondIntFeed, new Shape(rows, cols)))); + Assert.AreEqual((int)o, intResult); // Testing `operator >(Tensor x, int y) c = tf.reduce_sum(tf.reduce_sum(tf.cast(a > intThreshold, tf.int32), 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, - new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols)))); - Assert.AreEqual((int)o, intResult); - } + sess = tf.Session(); + o = sess.run(c, + new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols)))); + Assert.AreEqual((int)o, intResult); // Testing `operator >(int x, Tensor y) c = tf.reduce_sum(tf.reduce_sum(tf.cast(intThreshold > a, tf.int32), 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, - new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols)))); - Assert.AreEqual((int)o, intResultTwo); - } + sess = tf.Session(); + o = sess.run(c, + new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols)))); + Assert.AreEqual((int)o, intResultTwo); #endregion #region floatTest @@ -946,41 +812,33 @@ namespace TensorFlowNET.UnitTest.Basics b = tf.placeholder(tf.float32, shape: new Shape(rows, cols)); c = tf.reduce_sum(tf.reduce_sum(tf.cast(tf.greater(a, b), tf.int32), 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, - new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols))), - new FeedItem(b, new NDArray(secondFloatFeed, new Shape(rows, cols)))); - Assert.AreEqual((int)o, floatResult); - } + sess = tf.Session(); + o = sess.run(c, + new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols))), + new FeedItem(b, new NDArray(secondFloatFeed, new Shape(rows, cols)))); + Assert.AreEqual((int)o, floatResult); // Testing `operator >(Tensor x, Tensor y) c = tf.reduce_sum(tf.reduce_sum(tf.cast(a > b, tf.int32), 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, - new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols))), - new FeedItem(b, new NDArray(secondFloatFeed, new Shape(rows, cols)))); - Assert.AreEqual((int)o, floatResult); - } + sess = tf.Session(); + o = sess.run(c, + new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols))), + new FeedItem(b, new NDArray(secondFloatFeed, new Shape(rows, cols)))); + Assert.AreEqual((int)o, floatResult); // Testing `operator >(Tensor x, float y) c = tf.reduce_sum(tf.reduce_sum(tf.cast(a > floatThreshold, tf.int32), 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, - new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols)))); - Assert.AreEqual((int)o, floatResult); - } + sess = tf.Session(); + o = sess.run(c, + new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols)))); + Assert.AreEqual((int)o, floatResult); // Testing `operator >(float x, Tensor y) c = tf.reduce_sum(tf.reduce_sum(tf.cast(floatThreshold > a, tf.int32), 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, - new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols)))); - Assert.AreEqual((int)o, floatResultTwo); - } + sess = tf.Session(); + o = sess.run(c, + new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols)))); + Assert.AreEqual((int)o, floatResultTwo); #endregion #region doubleTest @@ -995,41 +853,33 @@ namespace TensorFlowNET.UnitTest.Basics b = tf.placeholder(tf.float64, shape: new Shape(rows, cols)); c = tf.reduce_sum(tf.reduce_sum(tf.cast(tf.greater(a, b), tf.int32), 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, - new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols))), - new FeedItem(b, new NDArray(secondDoubleFeed, new Shape(rows, cols)))); - Assert.AreEqual((int)o, doubleResult); - } + sess = tf.Session(); + o = sess.run(c, + new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols))), + new FeedItem(b, new NDArray(secondDoubleFeed, new Shape(rows, cols)))); + Assert.AreEqual((int)o, doubleResult); // Testing `operator >(Tensor x, Tensor y) c = tf.reduce_sum(tf.reduce_sum(tf.cast(a > b, tf.int32), 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, - new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols))), - new FeedItem(b, new NDArray(secondDoubleFeed, new Shape(rows, cols)))); - Assert.AreEqual((int)o, doubleResult); - } + sess = tf.Session(); + o = sess.run(c, + new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols))), + new FeedItem(b, new NDArray(secondDoubleFeed, new Shape(rows, cols)))); + Assert.AreEqual((int)o, doubleResult); // Testing `operator >(Tensor x, double y) c = tf.reduce_sum(tf.reduce_sum(tf.cast(a > doubleThreshold, tf.int32), 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, - new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols)))); - Assert.AreEqual((int)o, doubleResult); - } + sess = tf.Session(); + o = sess.run(c, + new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols)))); + Assert.AreEqual((int)o, doubleResult); // Testing `operator >(double x, Tensor y) c = tf.reduce_sum(tf.reduce_sum(tf.cast(doubleThreshold > a, tf.int32), 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, - new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols)))); - Assert.AreEqual((int)o, doubleResultTwo); - } + sess = tf.Session(); + o = sess.run(c, + new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols)))); + Assert.AreEqual((int)o, doubleResultTwo); #endregion } @@ -1051,41 +901,33 @@ namespace TensorFlowNET.UnitTest.Basics var b = tf.placeholder(tf.int32, shape: new Shape(rows, cols)); var c = tf.reduce_sum(tf.reduce_sum(tf.cast(tf.less(a, b), tf.int32), 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, - new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols))), - new FeedItem(b, new NDArray(secondIntFeed, new Shape(rows, cols)))); - Assert.AreEqual((int)o, intResult); - } + var sess = tf.Session(); + var o = sess.run(c, + new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols))), + new FeedItem(b, new NDArray(secondIntFeed, new Shape(rows, cols)))); + Assert.AreEqual((int)o, intResult); // Testing `operator <(Tensor x, Tensor y) c = tf.reduce_sum(tf.reduce_sum(tf.cast(a < b, tf.int32), 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, - new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols))), - new FeedItem(b, new NDArray(secondIntFeed, new Shape(rows, cols)))); - Assert.AreEqual((int)o, intResult); - } + sess = tf.Session(); + o = sess.run(c, + new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols))), + new FeedItem(b, new NDArray(secondIntFeed, new Shape(rows, cols)))); + Assert.AreEqual((int)o, intResult); // Testing `operator <(Tensor x, int y) c = tf.reduce_sum(tf.reduce_sum(tf.cast(a < intThreshold, tf.int32), 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, - new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols)))); - Assert.AreEqual((int)o, intResult); - } + sess = tf.Session(); + o = sess.run(c, + new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols)))); + Assert.AreEqual((int)o, intResult); // Testing `operator <(int x, Tensor y) c = tf.reduce_sum(tf.reduce_sum(tf.cast(intThreshold < a, tf.int32), 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, - new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols)))); - Assert.AreEqual((int)o, intResultTwo); - } + sess = tf.Session(); + o = sess.run(c, + new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols)))); + Assert.AreEqual((int)o, intResultTwo); #endregion #region floatTest @@ -1100,41 +942,33 @@ namespace TensorFlowNET.UnitTest.Basics b = tf.placeholder(tf.float32, shape: new Shape(rows, cols)); c = tf.reduce_sum(tf.reduce_sum(tf.cast(tf.less(a, b), tf.int32), 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, - new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols))), - new FeedItem(b, new NDArray(secondFloatFeed, new Shape(rows, cols)))); - Assert.AreEqual((int)o, floatResult); - } + sess = tf.Session(); + o = sess.run(c, + new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols))), + new FeedItem(b, new NDArray(secondFloatFeed, new Shape(rows, cols)))); + Assert.AreEqual((int)o, floatResult); // Testing `operator <(Tensor x, Tensor y) c = tf.reduce_sum(tf.reduce_sum(tf.cast(a < b, tf.int32), 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, - new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols))), - new FeedItem(b, new NDArray(secondFloatFeed, new Shape(rows, cols)))); - Assert.AreEqual((int)o, floatResult); - } + sess = tf.Session(); + o = sess.run(c, + new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols))), + new FeedItem(b, new NDArray(secondFloatFeed, new Shape(rows, cols)))); + Assert.AreEqual((int)o, floatResult); // Testing `operator <(Tensor x, float y) c = tf.reduce_sum(tf.reduce_sum(tf.cast(a < floatThreshold, tf.int32), 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, - new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols)))); - Assert.AreEqual((int)o, floatResult); - } + sess = tf.Session(); + o = sess.run(c, + new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols)))); + Assert.AreEqual((int)o, floatResult); // Testing `operator <(float x, Tensor y) c = tf.reduce_sum(tf.reduce_sum(tf.cast(floatThreshold < a, tf.int32), 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, - new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols)))); - Assert.AreEqual((int)o, floatResultTwo); - } + sess = tf.Session(); + o = sess.run(c, + new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols)))); + Assert.AreEqual((int)o, floatResultTwo); #endregion #region doubleTest @@ -1149,41 +983,33 @@ namespace TensorFlowNET.UnitTest.Basics b = tf.placeholder(tf.float64, shape: new Shape(rows, cols)); c = tf.reduce_sum(tf.reduce_sum(tf.cast(tf.less(a, b), tf.int32), 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, - new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols))), - new FeedItem(b, new NDArray(secondDoubleFeed, new Shape(rows, cols)))); - Assert.AreEqual((int)o, doubleResult); - } + sess = tf.Session(); + o = sess.run(c, + new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols))), + new FeedItem(b, new NDArray(secondDoubleFeed, new Shape(rows, cols)))); + Assert.AreEqual((int)o, doubleResult); // Testing `operator <(Tensor x, Tensor y) c = tf.reduce_sum(tf.reduce_sum(tf.cast(a < b, tf.int32), 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, - new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols))), - new FeedItem(b, new NDArray(secondDoubleFeed, new Shape(rows, cols)))); - Assert.AreEqual((int)o, doubleResult); - } + sess = tf.Session(); + o = sess.run(c, + new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols))), + new FeedItem(b, new NDArray(secondDoubleFeed, new Shape(rows, cols)))); + Assert.AreEqual((int)o, doubleResult); // Testing `operator <(Tensor x, double y) c = tf.reduce_sum(tf.reduce_sum(tf.cast(a < doubleThreshold, tf.int32), 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, - new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols)))); - Assert.AreEqual((int)o, doubleResult); - } + sess = tf.Session(); + o = sess.run(c, + new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols)))); + Assert.AreEqual((int)o, doubleResult); // Testing `operator <(double x, Tensor y) c = tf.reduce_sum(tf.reduce_sum(tf.cast(doubleThreshold < a, tf.int32), 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, - new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols)))); - Assert.AreEqual((int)o, doubleResultTwo); - } + sess = tf.Session(); + o = sess.run(c, + new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols)))); + Assert.AreEqual((int)o, doubleResultTwo); #endregion } @@ -1205,41 +1031,33 @@ namespace TensorFlowNET.UnitTest.Basics var b = tf.placeholder(tf.int32, shape: new Shape(rows, cols)); var c = tf.reduce_sum(tf.reduce_sum(tf.cast(tf.greater_equal(a, b), tf.int32), 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, - new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols))), - new FeedItem(b, new NDArray(secondIntFeed, new Shape(rows, cols)))); - Assert.AreEqual((int)o, intResult); - } + var sess = tf.Session(); + var o = sess.run(c, + new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols))), + new FeedItem(b, new NDArray(secondIntFeed, new Shape(rows, cols)))); + Assert.AreEqual((int)o, intResult); // Testing `operator >=(Tensor x, Tensor y) c = tf.reduce_sum(tf.reduce_sum(tf.cast(a >= b, tf.int32), 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, - new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols))), - new FeedItem(b, new NDArray(secondIntFeed, new Shape(rows, cols)))); - Assert.AreEqual((int)o, intResult); - } + sess = tf.Session(); + o = sess.run(c, + new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols))), + new FeedItem(b, new NDArray(secondIntFeed, new Shape(rows, cols)))); + Assert.AreEqual((int)o, intResult); // Testing `operator >=(Tensor x, int y) c = tf.reduce_sum(tf.reduce_sum(tf.cast(a >= intThreshold, tf.int32), 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, + sess = tf.Session(); + o = sess.run(c, new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols)))); - Assert.AreEqual((int)o, intResult); - } + Assert.AreEqual((int)o, intResult); // Testing `operator >=(int x, Tensor y) c = tf.reduce_sum(tf.reduce_sum(tf.cast(intThreshold >= a, tf.int32), 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, + sess = tf.Session(); + o = sess.run(c, new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols)))); - Assert.AreEqual((int)o, intResultTwo); - } + Assert.AreEqual((int)o, intResultTwo); #endregion #region floatTest @@ -1254,41 +1072,33 @@ namespace TensorFlowNET.UnitTest.Basics b = tf.placeholder(tf.float32, shape: new Shape(rows, cols)); c = tf.reduce_sum(tf.reduce_sum(tf.cast(tf.greater_equal(a, b), tf.int32), 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, + sess = tf.Session(); + o = sess.run(c, new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols))), new FeedItem(b, new NDArray(secondFloatFeed, new Shape(rows, cols)))); - Assert.AreEqual((int)o, floatResult); - } + Assert.AreEqual((int)o, floatResult); // Testing `operator >=(Tensor x, Tensor y) c = tf.reduce_sum(tf.reduce_sum(tf.cast(a >= b, tf.int32), 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, + sess = tf.Session(); + o = sess.run(c, new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols))), new FeedItem(b, new NDArray(secondFloatFeed, new Shape(rows, cols)))); - Assert.AreEqual((int)o, floatResult); - } + Assert.AreEqual((int)o, floatResult); // Testing `operator >=(Tensor x, float y) c = tf.reduce_sum(tf.reduce_sum(tf.cast(a >= floatThreshold, tf.int32), 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, + sess = tf.Session(); + sess.run(c, new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols)))); - Assert.AreEqual((int)o, floatResult); - } + Assert.AreEqual((int)o, floatResult); // Testing `operator >=(float x, Tensor y) c = tf.reduce_sum(tf.reduce_sum(tf.cast(floatThreshold >= a, tf.int32), 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, + sess = tf.Session(); + o = sess.run(c, new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols)))); - Assert.AreEqual((int)o, floatResultTwo); - } + Assert.AreEqual((int)o, floatResultTwo); #endregion #region doubleTest @@ -1303,41 +1113,33 @@ namespace TensorFlowNET.UnitTest.Basics b = tf.placeholder(tf.float64, shape: new Shape(rows, cols)); c = tf.reduce_sum(tf.reduce_sum(tf.cast(tf.greater_equal(a, b), tf.int32), 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, + sess = tf.Session(); + o = sess.run(c, new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols))), new FeedItem(b, new NDArray(secondDoubleFeed, new Shape(rows, cols)))); - Assert.AreEqual((int)o, doubleResult); - } + Assert.AreEqual((int)o, doubleResult); // Testing `operator >=(Tensor x, Tensor y) c = tf.reduce_sum(tf.reduce_sum(tf.cast(a >= b, tf.int32), 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, + sess = tf.Session(); + o = sess.run(c, new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols))), new FeedItem(b, new NDArray(secondDoubleFeed, new Shape(rows, cols)))); - Assert.AreEqual((int)o, doubleResult); - } + Assert.AreEqual((int)o, doubleResult); // Testing `operator >=(Tensor x, double y) c = tf.reduce_sum(tf.reduce_sum(tf.cast(a >= doubleThreshold, tf.int32), 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, + sess = tf.Session(); + o = sess.run(c, new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols)))); - Assert.AreEqual((int)o, doubleResult); - } + Assert.AreEqual((int)o, doubleResult); // Testing `operator >=(double x, Tensor y) c = tf.reduce_sum(tf.reduce_sum(tf.cast(doubleThreshold >= a, tf.int32), 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, + sess = tf.Session(); + o = sess.run(c, new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols)))); - Assert.AreEqual((int)o, doubleResultTwo); - } + Assert.AreEqual((int)o, doubleResultTwo); #endregion } @@ -1359,41 +1161,33 @@ namespace TensorFlowNET.UnitTest.Basics var b = tf.placeholder(tf.int32, shape: new Shape(rows, cols)); var c = tf.reduce_sum(tf.reduce_sum(tf.cast(tf.less_equal(a, b), tf.int32), 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, - new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols))), - new FeedItem(b, new NDArray(secondIntFeed, new Shape(rows, cols)))); - Assert.AreEqual((int)o, intResult); - } + var sess = tf.Session(); + var o = sess.run(c, + new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols))), + new FeedItem(b, new NDArray(secondIntFeed, new Shape(rows, cols)))); + Assert.AreEqual((int)o, intResult); // Testing `operator <=(Tensor x, Tensor y) c = tf.reduce_sum(tf.reduce_sum(tf.cast(a <= b, tf.int32), 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, + sess = tf.Session(); + o = sess.run(c, new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols))), new FeedItem(b, new NDArray(secondIntFeed, new Shape(rows, cols)))); - Assert.AreEqual((int)o, intResult); - } + Assert.AreEqual((int)o, intResult); // Testing `operator <=(Tensor x, int y) c = tf.reduce_sum(tf.reduce_sum(tf.cast(a <= intThreshold, tf.int32), 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, + sess = tf.Session(); + o = sess.run(c, new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols)))); - Assert.AreEqual((int)o, intResult); - } + Assert.AreEqual((int)o, intResult); // Testing `operator <=(int x, Tensor y) c = tf.reduce_sum(tf.reduce_sum(tf.cast(intThreshold <= a, tf.int32), 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, + sess = tf.Session(); + o = sess.run(c, new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols)))); - Assert.AreEqual((int)o, intResultTwo); - } + Assert.AreEqual((int)o, intResultTwo); #endregion #region floatTest @@ -1408,41 +1202,33 @@ namespace TensorFlowNET.UnitTest.Basics b = tf.placeholder(tf.float32, shape: new Shape(rows, cols)); c = tf.reduce_sum(tf.reduce_sum(tf.cast(tf.less_equal(a, b), tf.int32), 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, + sess = tf.Session(); + o = sess.run(c, new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols))), new FeedItem(b, new NDArray(secondFloatFeed, new Shape(rows, cols)))); - Assert.AreEqual((int)o, floatResult); - } + Assert.AreEqual((int)o, floatResult); // Testing `operator <=(Tensor x, Tensor y) c = tf.reduce_sum(tf.reduce_sum(tf.cast(a <= b, tf.int32), 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, + sess = tf.Session(); + o = sess.run(c, new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols))), new FeedItem(b, new NDArray(secondFloatFeed, new Shape(rows, cols)))); - Assert.AreEqual((int)o, floatResult); - } + Assert.AreEqual((int)o, floatResult); // Testing `operator <=(Tensor x, float y) c = tf.reduce_sum(tf.reduce_sum(tf.cast(a <= floatThreshold, tf.int32), 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, + sess = tf.Session(); + o = sess.run(c, new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols)))); - Assert.AreEqual((int)o, floatResult); - } + Assert.AreEqual((int)o, floatResult); // Testing `operator <=(float x, Tensor y) c = tf.reduce_sum(tf.reduce_sum(tf.cast(floatThreshold <= a, tf.int32), 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, + sess = tf.Session(); + o = sess.run(c, new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols)))); - Assert.AreEqual((int)o, floatResultTwo); - } + Assert.AreEqual((int)o, floatResultTwo); #endregion #region doubleTest @@ -1457,41 +1243,33 @@ namespace TensorFlowNET.UnitTest.Basics b = tf.placeholder(tf.float64, shape: new Shape(rows, cols)); c = tf.reduce_sum(tf.reduce_sum(tf.cast(tf.less_equal(a, b), tf.int32), 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, + sess = tf.Session(); + o = sess.run(c, new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols))), new FeedItem(b, new NDArray(secondDoubleFeed, new Shape(rows, cols)))); - Assert.AreEqual((int)o, doubleResult); - } + Assert.AreEqual((int)o, doubleResult); // Testing `operator <=(Tensor x, Tensor y) c = tf.reduce_sum(tf.reduce_sum(tf.cast(a <= b, tf.int32), 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, + sess = tf.Session(); + o = sess.run(c, new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols))), new FeedItem(b, new NDArray(secondDoubleFeed, new Shape(rows, cols)))); - Assert.AreEqual((int)o, doubleResult); - } + Assert.AreEqual((int)o, doubleResult); // Testing `operator <=(Tensor x, double y) c = tf.reduce_sum(tf.reduce_sum(tf.cast(a <= doubleThreshold, tf.int32), 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, + sess = tf.Session(); + o = sess.run(c, new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols)))); - Assert.AreEqual((int)o, doubleResult); - } + Assert.AreEqual((int)o, doubleResult); // Testing `operator <=(double x, Tensor y) c = tf.reduce_sum(tf.reduce_sum(tf.cast(doubleThreshold <= a, tf.int32), 1)); - using (var sess = tf.Session()) - { - var o = sess.run(c, + sess = tf.Session(); + o = sess.run(c, new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols)))); - Assert.AreEqual((int)o, doubleResultTwo); - } + Assert.AreEqual((int)o, doubleResultTwo); #endregion } diff --git a/test/TensorFlowNET.Graph.UnitTest/PythonTest.cs b/test/TensorFlowNET.Graph.UnitTest/PythonTest.cs index e49103a1..51379193 100644 --- a/test/TensorFlowNET.Graph.UnitTest/PythonTest.cs +++ b/test/TensorFlowNET.Graph.UnitTest/PythonTest.cs @@ -182,23 +182,21 @@ namespace TensorFlowNET.UnitTest // return self._eval_helper(tensors) // else: { - using (var sess = tf.Session()) + var sess = tf.Session(); + var ndarray = tensor.eval(sess); + if (typeof(T) == typeof(double)) { - var ndarray = tensor.eval(sess); - if (typeof(T) == typeof(double)) - { - double x = ndarray; - result = x; - } - else if (typeof(T) == typeof(int)) - { - int x = ndarray; - result = x; - } - else - { - result = ndarray; - } + double x = ndarray; + result = x; + } + else if (typeof(T) == typeof(int)) + { + int x = ndarray; + result = x; + } + else + { + result = ndarray; } return (T)result; diff --git a/test/TensorFlowNET.Native.UnitTest/Attributes/AttributesTestcs.cs b/test/TensorFlowNET.Native.UnitTest/Attributes/AttributesTestcs.cs index f1df976b..4db19ed5 100644 --- a/test/TensorFlowNET.Native.UnitTest/Attributes/AttributesTestcs.cs +++ b/test/TensorFlowNET.Native.UnitTest/Attributes/AttributesTestcs.cs @@ -48,7 +48,7 @@ namespace Tensorflow.Native.UnitTest private void EXPECT_TF_META(Operation oper, string attr_name, int expected_list_size, TF_AttrType expected_type, uint expected_total_size) { - var m = c_api.TF_OperationGetAttrMetadata(oper, attr_name, s_.Handle); + var m = c_api.TF_OperationGetAttrMetadata(oper, attr_name, s_); EXPECT_EQ(TF_Code.TF_OK, s_.Code); char e = expected_list_size >= 0 ? (char)1 : (char)0; /*EXPECT_EQ(e, m.is_list); @@ -63,7 +63,7 @@ namespace Tensorflow.Native.UnitTest var desc = init("string"); c_api.TF_SetAttrString(desc, "v", "bunny", 5); - var oper = c_api.TF_FinishOperation(desc, s_.Handle); + var oper = c_api.TF_FinishOperation(desc, s_); //ASSERT_EQ(TF_Code.TF_OK, s_.Code); //EXPECT_TF_META(oper, "v", -1, TF_AttrType.TF_ATTR_STRING, 5); //var value = new char[5]; @@ -86,8 +86,6 @@ namespace Tensorflow.Native.UnitTest public void Dispose() { - graph_.Dispose(); - s_.Dispose(); } } } diff --git a/test/TensorFlowNET.Native.UnitTest/CApiColocationTest.cs b/test/TensorFlowNET.Native.UnitTest/CApiColocationTest.cs index 09b88e13..c162cb72 100644 --- a/test/TensorFlowNET.Native.UnitTest/CApiColocationTest.cs +++ b/test/TensorFlowNET.Native.UnitTest/CApiColocationTest.cs @@ -59,7 +59,7 @@ namespace Tensorflow.Native.UnitTest private void VerifyCollocation(Operation op, string[] expected) { - var handle = c_api.TF_OperationGetAttrMetadata(op, "_class", s_.Handle); + var handle = c_api.TF_OperationGetAttrMetadata(op, "_class", s_); TF_AttrMetadata m = new TF_AttrMetadata(); if (expected.Length == 0) { @@ -98,8 +98,6 @@ namespace Tensorflow.Native.UnitTest public void Dispose() { - graph_.Dispose(); - s_.Dispose(); } } } diff --git a/test/TensorFlowNET.Native.UnitTest/CApiTest.cs b/test/TensorFlowNET.Native.UnitTest/CApiTest.cs index 2432ec1f..fb4ed482 100644 --- a/test/TensorFlowNET.Native.UnitTest/CApiTest.cs +++ b/test/TensorFlowNET.Native.UnitTest/CApiTest.cs @@ -45,10 +45,10 @@ namespace Tensorflow.Native.UnitTest => c_api.TF_AddInput(desc, input); protected Operation TF_FinishOperation(OperationDescription desc, Status s) - => c_api.TF_FinishOperation(desc, s.Handle); + => c_api.TF_FinishOperation(desc, s); protected void TF_SetAttrTensor(OperationDescription desc, string attrName, Tensor value, Status s) - => c_api.TF_SetAttrTensor(desc, attrName, value, s.Handle); + => c_api.TF_SetAttrTensor(desc, attrName, value, s); protected void TF_SetAttrType(OperationDescription desc, string attrName, TF_DataType dtype) => c_api.TF_SetAttrType(desc, attrName, dtype); diff --git a/test/TensorFlowNET.Native.UnitTest/Functions/FunctionTest.cs b/test/TensorFlowNET.Native.UnitTest/Functions/FunctionTest.cs index 265509ae..0872394b 100644 --- a/test/TensorFlowNET.Native.UnitTest/Functions/FunctionTest.cs +++ b/test/TensorFlowNET.Native.UnitTest/Functions/FunctionTest.cs @@ -18,7 +18,7 @@ namespace Tensorflow.Native.UnitTest string func_name_ = "MyFunc"; string func_node_name_ = "MyFunc_0"; Status s_; - IntPtr func_; + SafeFuncGraphHandle func_; [TestInitialize] public void Initialize() @@ -402,7 +402,7 @@ namespace Tensorflow.Native.UnitTest inputs.Length, inputs.ToArray(), outputs.Length, outputs.ToArray(), output_names == null || output_names.Length == 0 ? null : output_names, - IntPtr.Zero, null, s_.Handle); + IntPtr.Zero, null, s_); if (expect_failure) { @@ -413,7 +413,7 @@ namespace Tensorflow.Native.UnitTest ASSERT_EQ(TF_OK, s_.Code, s_.Message); ASSERT_NE(func_, IntPtr.Zero); ASSERT_EQ(func_name_, c_api.StringPiece(c_api.TF_FunctionName(func_))); - c_api.TF_GraphCopyFunction(host_graph_, func_, IntPtr.Zero, s_.Handle); + c_api.TF_GraphCopyFunction(host_graph_, func_, IntPtr.Zero, s_); ASSERT_EQ(TF_OK, s_.Code, s_.Message); } diff --git a/test/TensorFlowNET.Native.UnitTest/Gradients/GradientsTest.cs b/test/TensorFlowNET.Native.UnitTest/Gradients/GradientsTest.cs index 2cdd5019..79fa4489 100644 --- a/test/TensorFlowNET.Native.UnitTest/Gradients/GradientsTest.cs +++ b/test/TensorFlowNET.Native.UnitTest/Gradients/GradientsTest.cs @@ -44,18 +44,14 @@ namespace Tensorflow.Native.UnitTest private bool GetGraphDef(Graph graph, out GraphDef graph_def) { graph_def = null; - using (var s = new Status()) - { - using (var buffer = new Buffer()) - { - c_api.TF_GraphToGraphDef(graph, buffer.Handle, s.Handle); - bool ret = TF_GetCode(s) == TF_OK; - EXPECT_EQ(TF_OK, TF_GetCode(s)); - if (ret) - graph_def = GraphDef.Parser.ParseFrom(buffer.ToArray()); - return ret; - } - } + var s = new Status(); + var buffer = new Buffer(); + c_api.TF_GraphToGraphDef(graph, buffer, s); + bool ret = TF_GetCode(s) == TF_OK; + EXPECT_EQ(TF_OK, TF_GetCode(s)); + if (ret) + graph_def = GraphDef.Parser.ParseFrom(buffer.ToArray()); + return ret; } private void RunGraphsAndCompareOutputs(TF_Output[] grad_outputs, TF_Output[] expected_grad_outputs) @@ -111,9 +107,9 @@ namespace Tensorflow.Native.UnitTest IntPtr[] handles = new IntPtr[2] { IntPtr.Zero, IntPtr.Zero }; c_api.TF_AddGradientsWithPrefix(graph_, prefix, outputs, noutputs, inputs, - ninputs, grad_inputs, s_.Handle, handles); + ninputs, grad_inputs, s_, handles); - var op = new Operation(handles[0]); + // var op = new Operation(handles[0]); } else { @@ -275,9 +271,6 @@ namespace Tensorflow.Native.UnitTest public void Dispose() { - graph_.Dispose(); - expected_graph_.Dispose(); - s_.Dispose(); } } } diff --git a/test/TensorFlowNET.Native.UnitTest/Graphs/GraphBuildTest.cs b/test/TensorFlowNET.Native.UnitTest/Graphs/GraphBuildTest.cs index 751dc355..ed39882e 100644 --- a/test/TensorFlowNET.Native.UnitTest/Graphs/GraphBuildTest.cs +++ b/test/TensorFlowNET.Native.UnitTest/Graphs/GraphBuildTest.cs @@ -9,7 +9,7 @@ namespace Tensorflow.Native.UnitTest [TestMethod, Ignore("Waiting to merge https://github.com/tensorflow/tensorflow/pull/43383")] public void UpdateEdge() { - using var graph = new Graph().as_default(); + var graph = new Graph().as_default(); var one = tf.constant(1, name: "one"); var two = tf.constant(2, name: "two"); diff --git a/test/TensorFlowNET.Native.UnitTest/Graphs/GraphTest.cs b/test/TensorFlowNET.Native.UnitTest/Graphs/GraphTest.cs index e40fd5c8..33b5cd9f 100644 --- a/test/TensorFlowNET.Native.UnitTest/Graphs/GraphTest.cs +++ b/test/TensorFlowNET.Native.UnitTest/Graphs/GraphTest.cs @@ -35,7 +35,7 @@ namespace Tensorflow.Native.UnitTest EXPECT_EQ(attr_value.Type, DataType.DtInt32); // Test not found errors in TF_Operation*() query functions. - EXPECT_EQ(-1, c_api.TF_OperationOutputListLength(feed, "bogus", s.Handle)); + EXPECT_EQ(-1, c_api.TF_OperationOutputListLength(feed, "bogus", s)); EXPECT_EQ(TF_Code.TF_INVALID_ARGUMENT, s.Code); Assert.IsFalse(c_test_util.GetAttrValue(feed, "missing", ref attr_value, s)); EXPECT_EQ("Operation 'feed' has no attr named 'missing'.", s.Message); @@ -191,9 +191,6 @@ namespace Tensorflow.Native.UnitTest ASSERT_TRUE(found_scalar_const); ASSERT_TRUE(found_add); ASSERT_TRUE(found_neg); - - graph.Dispose(); - s.Dispose(); } /// @@ -213,16 +210,15 @@ namespace Tensorflow.Native.UnitTest // Export to a GraphDef. var graph_def = new Buffer(); - c_api.TF_GraphToGraphDef(graph, graph_def.Handle, s.Handle); + c_api.TF_GraphToGraphDef(graph, graph_def, s); EXPECT_EQ(TF_Code.TF_OK, s.Code); // Import it, with a prefix, in a fresh graph. - graph.Dispose(); graph = new Graph().as_default(); using (var opts = c_api.TF_NewImportGraphDefOptions()) { c_api.TF_ImportGraphDefOptionsSetPrefix(opts, "imported"); - c_api.TF_GraphImportGraphDef(graph, graph_def.Handle, opts, s.Handle); + c_api.TF_GraphImportGraphDef(graph, graph_def, opts, s); EXPECT_EQ(TF_Code.TF_OK, s.Code); } @@ -265,7 +261,7 @@ namespace Tensorflow.Native.UnitTest EXPECT_EQ(2, c_api.TF_ImportGraphDefOptionsNumReturnOutputs(opts)); c_api.TF_ImportGraphDefOptionsAddReturnOperation(opts, "scalar"); EXPECT_EQ(1, c_api.TF_ImportGraphDefOptionsNumReturnOperations(opts)); - var results = c_api.TF_GraphImportGraphDefWithResults(graph, graph_def.Handle, opts, s.Handle); + var results = c_api.TF_GraphImportGraphDefWithResults(graph, graph_def, opts, s); EXPECT_EQ(TF_Code.TF_OK, s.Code); return results; @@ -305,7 +301,7 @@ namespace Tensorflow.Native.UnitTest c_api.TF_ImportGraphDefOptionsSetPrefix(opts, "imported3"); c_api.TF_ImportGraphDefOptionsAddControlDependency(opts, feed); c_api.TF_ImportGraphDefOptionsAddControlDependency(opts, feed2); - c_api.TF_GraphImportGraphDef(graph, graph_def.Handle, opts, s.Handle); + c_api.TF_GraphImportGraphDef(graph, graph_def, opts, s); EXPECT_EQ(TF_Code.TF_OK, s.Code); } @@ -330,7 +326,7 @@ namespace Tensorflow.Native.UnitTest // Export to a graph def so we can import a graph with control dependencies graph_def = new Buffer(); - c_api.TF_GraphToGraphDef(graph, graph_def.Handle, s.Handle); + c_api.TF_GraphToGraphDef(graph, graph_def, s); EXPECT_EQ(TF_Code.TF_OK, s.Code); // Import again, with remapped control dependency, into the same graph @@ -338,7 +334,7 @@ namespace Tensorflow.Native.UnitTest { c_api.TF_ImportGraphDefOptionsSetPrefix(opts, "imported4"); c_api.TF_ImportGraphDefOptionsRemapControlDependency(opts, "imported/feed", feed); - c_api.TF_GraphImportGraphDef(graph, graph_def.Handle, opts, s.Handle); + c_api.TF_GraphImportGraphDef(graph, graph_def, opts, s); ASSERT_EQ(TF_Code.TF_OK, s.Code); } @@ -380,7 +376,6 @@ namespace Tensorflow.Native.UnitTest ASSERT_EQ(TF_Code.TF_OK, s.Code); // Import it in a fresh graph with return outputs. - graph.Dispose(); graph = new Graph().as_default(); var opts = new ImportGraphDefOptions(); opts.AddReturnOutput("feed", 0); @@ -401,11 +396,6 @@ namespace Tensorflow.Native.UnitTest EXPECT_EQ(0, return_outputs[0].index); EXPECT_EQ(scalar, return_outputs[1].oper); EXPECT_EQ(0, return_outputs[1].index); - - opts.Dispose(); - graph_def.Dispose(); - graph.Dispose(); - s.Dispose(); } /// @@ -422,16 +412,14 @@ namespace Tensorflow.Native.UnitTest public void ImportGraphMeta() { var dir = "my-save-dir/"; - using (var sess = tf.Session()) - { - var new_saver = tf.train.import_meta_graph(dir + "my-model-10000.meta"); - new_saver.restore(sess, dir + "my-model-10000"); - var labels = tf.constant(0, dtype: tf.int32, shape: new int[] { 100 }, name: "labels"); - var batch_size = tf.size(labels); - var logits = tf.get_collection("logits")[0] as Tensor; - var loss = tf.losses.sparse_softmax_cross_entropy(labels: labels, - logits: logits); - } + var sess = tf.Session(); + var new_saver = tf.train.import_meta_graph(dir + "my-model-10000.meta"); + new_saver.restore(sess, dir + "my-model-10000"); + var labels = tf.constant(0, dtype: tf.int32, shape: new int[] { 100 }, name: "labels"); + var batch_size = tf.size(labels); + var logits = tf.get_collection("logits")[0] as Tensor; + var loss = tf.losses.sparse_softmax_cross_entropy(labels: labels, + logits: logits); } } } diff --git a/test/TensorFlowNET.Native.UnitTest/Sessions/CSession.cs b/test/TensorFlowNET.Native.UnitTest/Sessions/CSession.cs index ee0f6edf..e7957100 100644 --- a/test/TensorFlowNET.Native.UnitTest/Sessions/CSession.cs +++ b/test/TensorFlowNET.Native.UnitTest/Sessions/CSession.cs @@ -11,7 +11,7 @@ namespace Tensorflow.Native.UnitTest /// public class CSession { - private IntPtr session_; + private SafeSessionHandle session_; private List inputs_ = new List(); private List input_values_ = new List(); @@ -22,11 +22,8 @@ namespace Tensorflow.Native.UnitTest public CSession(Graph graph, Status s, bool user_XLA = false) { - lock (Locks.ProcessWide) - { - var config = new ConfigProto { InterOpParallelismThreads = 4 }; - session_ = new Session(graph, config, s); - } + var config = new ConfigProto { InterOpParallelismThreads = 4 }; + session_ = new Session(graph, config, s); } public void SetInputs(Dictionary inputs) @@ -85,7 +82,7 @@ namespace Tensorflow.Native.UnitTest c_api.TF_SessionRun(session_, null, inputs_ptr, input_values_ptr, inputs_ptr.Length, outputs_ptr, output_values_ptr, outputs_.Count, targets_ptr, targets_.Count, - IntPtr.Zero, s.Handle); + IntPtr.Zero, s); s.Check(); diff --git a/test/TensorFlowNET.Native.UnitTest/Sessions/SessionTest.cs b/test/TensorFlowNET.Native.UnitTest/Sessions/SessionTest.cs index 066c705c..74f9366c 100644 --- a/test/TensorFlowNET.Native.UnitTest/Sessions/SessionTest.cs +++ b/test/TensorFlowNET.Native.UnitTest/Sessions/SessionTest.cs @@ -14,8 +14,8 @@ namespace Tensorflow.Native.UnitTest.Sessions [TestMethod] public void Session() { - using var s = new Status(); - using var graph = new Graph(); + var s = new Status(); + var graph = new Graph(); // Make a placeholder operation. var feed = c_test_util.Placeholder(graph, s); diff --git a/test/TensorFlowNET.Native.UnitTest/Tensors/TensorTest.cs b/test/TensorFlowNET.Native.UnitTest/Tensors/TensorTest.cs index 74fc8da4..6ccc6cdd 100644 --- a/test/TensorFlowNET.Native.UnitTest/Tensors/TensorTest.cs +++ b/test/TensorFlowNET.Native.UnitTest/Tensors/TensorTest.cs @@ -139,45 +139,45 @@ namespace Tensorflow.Native.UnitTest.Tensors var feed_out_0 = new TF_Output(feed, 0); // Fetch the shape, it should be completely unknown. - int num_dims = c_api.TF_GraphGetTensorNumDims(graph, feed_out_0, s.Handle); + int num_dims = c_api.TF_GraphGetTensorNumDims(graph, feed_out_0, s); Assert.IsTrue(s.Code == TF_Code.TF_OK); EXPECT_EQ(-1, num_dims); // Set the shape to be unknown, expect no change. - c_api.TF_GraphSetTensorShape(graph, feed_out_0, null, -1, s.Handle); + c_api.TF_GraphSetTensorShape(graph, feed_out_0, null, -1, s); Assert.IsTrue(s.Code == TF_Code.TF_OK); - num_dims = c_api.TF_GraphGetTensorNumDims(graph, feed_out_0, s.Handle); + num_dims = c_api.TF_GraphGetTensorNumDims(graph, feed_out_0, s); EXPECT_EQ(-1, num_dims); // Set the shape to be 2 x Unknown long[] dims = { 2, -1 }; - c_api.TF_GraphSetTensorShape(graph, feed_out_0, dims, dims.Length, s.Handle); + c_api.TF_GraphSetTensorShape(graph, feed_out_0, dims, dims.Length, s); Assert.IsTrue(s.Code == TF_Code.TF_OK); - num_dims = c_api.TF_GraphGetTensorNumDims(graph, feed_out_0, s.Handle); + num_dims = c_api.TF_GraphGetTensorNumDims(graph, feed_out_0, s); EXPECT_EQ(2, num_dims); // Get the dimension vector appropriately. var returned_dims = new long[dims.Length]; - c_api.TF_GraphGetTensorShape(graph, feed_out_0, returned_dims, num_dims, s.Handle); + c_api.TF_GraphGetTensorShape(graph, feed_out_0, returned_dims, num_dims, s); Assert.IsTrue(s.Code == TF_Code.TF_OK); Assert.IsTrue(Enumerable.SequenceEqual(dims, returned_dims)); // Set to a new valid shape: [2, 3] dims[1] = 3; - c_api.TF_GraphSetTensorShape(graph, feed_out_0, dims, dims.Length, s.Handle); + c_api.TF_GraphSetTensorShape(graph, feed_out_0, dims, dims.Length, s); Assert.IsTrue(s.Code == TF_Code.TF_OK); // Fetch and see that the new value is returned. - c_api.TF_GraphGetTensorShape(graph, feed_out_0, returned_dims, num_dims, s.Handle); + c_api.TF_GraphGetTensorShape(graph, feed_out_0, returned_dims, num_dims, s); Assert.IsTrue(s.Code == TF_Code.TF_OK); Assert.IsTrue(Enumerable.SequenceEqual(dims, returned_dims)); // Try to set 'unknown' with unknown rank on the shape and see that // it doesn't change. - c_api.TF_GraphSetTensorShape(graph, feed_out_0, null, -1, s.Handle); + c_api.TF_GraphSetTensorShape(graph, feed_out_0, null, -1, s); Assert.IsTrue(s.Code == TF_Code.TF_OK); - c_api.TF_GraphGetTensorShape(graph, feed_out_0, returned_dims, num_dims, s.Handle); + c_api.TF_GraphGetTensorShape(graph, feed_out_0, returned_dims, num_dims, s); Assert.IsTrue(s.Code == TF_Code.TF_OK); EXPECT_EQ(2, num_dims); EXPECT_EQ(2, (int)returned_dims[0]); @@ -187,21 +187,21 @@ namespace Tensorflow.Native.UnitTest.Tensors // it doesn't change. dims[0] = -1; dims[1] = -1; - c_api.TF_GraphSetTensorShape(graph, feed_out_0, dims, 2, s.Handle); + c_api.TF_GraphSetTensorShape(graph, feed_out_0, dims, 2, s); Assert.IsTrue(s.Code == TF_Code.TF_OK); - c_api.TF_GraphGetTensorShape(graph, feed_out_0, returned_dims, num_dims, s.Handle); + c_api.TF_GraphGetTensorShape(graph, feed_out_0, returned_dims, num_dims, s); Assert.IsTrue(s.Code == TF_Code.TF_OK); EXPECT_EQ(2, num_dims); EXPECT_EQ(2, (int)returned_dims[0]); EXPECT_EQ(3, (int)returned_dims[1]); // Try to fetch a shape with the wrong num_dims - c_api.TF_GraphGetTensorShape(graph, feed_out_0, returned_dims, 5, s.Handle); + c_api.TF_GraphGetTensorShape(graph, feed_out_0, returned_dims, 5, s); Assert.IsTrue(s.Code == TF_Code.TF_INVALID_ARGUMENT); // Try to set an invalid shape (cannot change 2x3 to a 2x5). dims[1] = 5; - c_api.TF_GraphSetTensorShape(graph, feed_out_0, dims, 2, s.Handle); + c_api.TF_GraphSetTensorShape(graph, feed_out_0, dims, 2, s); Assert.IsTrue(s.Code == TF_Code.TF_INVALID_ARGUMENT); // Test for a scalar. @@ -209,14 +209,13 @@ namespace Tensorflow.Native.UnitTest.Tensors Assert.IsTrue(s.Code == TF_Code.TF_OK); var three_out_0 = new TF_Output(three, 0); - num_dims = c_api.TF_GraphGetTensorNumDims(graph, three_out_0, s.Handle); + num_dims = c_api.TF_GraphGetTensorNumDims(graph, three_out_0, s); Assert.IsTrue(s.Code == TF_Code.TF_OK); EXPECT_EQ(0, num_dims); - c_api.TF_GraphGetTensorShape(graph, feed_out_0, dims, num_dims, s.Handle); + c_api.TF_GraphGetTensorShape(graph, feed_out_0, dims, num_dims, s); Assert.IsTrue(s.Code == TF_Code.TF_INVALID_ARGUMENT); graph.Exit(); - s.Dispose(); } } } \ No newline at end of file diff --git a/test/TensorFlowNET.Native.UnitTest/c_test_util.cs b/test/TensorFlowNET.Native.UnitTest/c_test_util.cs index 50e747e9..4044046b 100644 --- a/test/TensorFlowNET.Native.UnitTest/c_test_util.cs +++ b/test/TensorFlowNET.Native.UnitTest/c_test_util.cs @@ -23,7 +23,7 @@ namespace Tensorflow.Native.UnitTest c_api.TF_AddInputList(desc, inputs, inputs.Length); - var op = c_api.TF_FinishOperation(desc, s.Handle); + var op = c_api.TF_FinishOperation(desc, s); s.Check(); return op; @@ -33,37 +33,29 @@ namespace Tensorflow.Native.UnitTest [SuppressMessage("ReSharper", "RedundantAssignment")] public static bool GetAttrValue(Operation oper, string attr_name, ref AttrValue attr_value, Status s) { - lock (Locks.ProcessWide) - { - using (var buffer = new Buffer()) - { - c_api.TF_OperationGetAttrValueProto(oper, attr_name, buffer.Handle, s.Handle); - attr_value = AttrValue.Parser.ParseFrom(buffer.ToArray()); - } + var buffer = new Buffer(); - return s.Code == TF_Code.TF_OK; - } + c_api.TF_OperationGetAttrValueProto(oper, attr_name, buffer, s); + attr_value = AttrValue.Parser.ParseFrom(buffer.ToArray()); + + return s.Code == TF_Code.TF_OK; } public static GraphDef GetGraphDef(Graph graph) { - lock (Locks.ProcessWide) - { - using (var s = new Status()) - using (var buffer = new Buffer()) - { - c_api.TF_GraphToGraphDef(graph, buffer.Handle, s.Handle); - s.Check(); - return GraphDef.Parser.ParseFrom(buffer.ToArray()); - } - } + var s = new Status(); + var buffer = new Buffer(); + + c_api.TF_GraphToGraphDef(graph, buffer, s); + s.Check(); + return GraphDef.Parser.ParseFrom(buffer.ToArray()); } - public static FunctionDef GetFunctionDef(IntPtr func) + public static FunctionDef GetFunctionDef(SafeFuncGraphHandle func) { - using var s = new Status(); - using var buffer = new Buffer(); - c_api.TF_FunctionToFunctionDef(func, buffer.Handle, s.Handle); + var s = new Status(); + var buffer = new Buffer(); + c_api.TF_FunctionToFunctionDef(func, buffer, s); s.Check(true); var func_def = FunctionDef.Parser.ParseFrom(buffer.ToArray()); return func_def; @@ -192,7 +184,7 @@ namespace Tensorflow.Native.UnitTest OperationDescription desc = c_api.TF_NewOperation(graph, "Neg", name); var neg_input = new TF_Output(n, 0); c_api.TF_AddInput(desc, neg_input); - var op = c_api.TF_FinishOperation(desc, s.Handle); + var op = c_api.TF_FinishOperation(desc, s); s.Check(); return op; @@ -210,7 +202,7 @@ namespace Tensorflow.Native.UnitTest c_api.TF_SetAttrShape(desc, "shape", dims, dims.Length); } - var op = c_api.TF_FinishOperation(desc, s.Handle); + var op = c_api.TF_FinishOperation(desc, s); s.Check(); return op; @@ -222,10 +214,10 @@ namespace Tensorflow.Native.UnitTest lock (Locks.ProcessWide) { var desc = c_api.TF_NewOperation(graph, "Const", name); - c_api.TF_SetAttrTensor(desc, "value", t, s.Handle); + c_api.TF_SetAttrTensor(desc, "value", t, s); s.Check(); c_api.TF_SetAttrType(desc, "dtype", t.dtype); - var op = c_api.TF_FinishOperation(desc, s.Handle); + var op = c_api.TF_FinishOperation(desc, s); s.Check(); return op; diff --git a/test/TensorFlowNET.UnitTest/Basics/TrainSaverTest.cs b/test/TensorFlowNET.UnitTest/Basics/TrainSaverTest.cs index 2a4a79dc..ca073e1e 100644 --- a/test/TensorFlowNET.UnitTest/Basics/TrainSaverTest.cs +++ b/test/TensorFlowNET.UnitTest/Basics/TrainSaverTest.cs @@ -17,10 +17,8 @@ namespace TensorFlowNET.UnitTest.Basics public void ImportGraph() { - using (var sess = tf.Session()) - { - var new_saver = tf.train.import_meta_graph("C:/tmp/my-model.meta"); - } + var sess = tf.Session(); + var new_saver = tf.train.import_meta_graph("C:/tmp/my-model.meta"); //tf.train.export_meta_graph(filename: "linear_regression.meta.bin"); // import meta @@ -60,14 +58,12 @@ namespace TensorFlowNET.UnitTest.Basics // Add ops to save and restore all the variables. var saver = tf.train.Saver(); - using (var sess = tf.Session()) - { - sess.run(init_op); + var sess = tf.Session(); + sess.run(init_op); - // Save the variables to disk. - var save_path = saver.save(sess, "/tmp/model1.ckpt"); - Console.WriteLine($"Model saved in path: {save_path}"); - } + // Save the variables to disk. + var save_path = saver.save(sess, "/tmp/model1.ckpt"); + Console.WriteLine($"Model saved in path: {save_path}"); } public void Save2() @@ -84,17 +80,15 @@ namespace TensorFlowNET.UnitTest.Basics // Add ops to save and restore all the variables. var saver = tf.train.Saver(); - using (var sess = tf.Session()) - { - sess.run(init_op); - // o some work with the model. - inc_v1.op.run(); - dec_v2.op.run(); - - // Save the variables to disk. - var save_path = saver.save(sess, "/tmp/model2.ckpt"); - Console.WriteLine($"Model saved in path: {save_path}"); - } + var sess = tf.Session(); + sess.run(init_op); + // o some work with the model. + inc_v1.op.run(); + dec_v2.op.run(); + + // Save the variables to disk. + var save_path = saver.save(sess, "/tmp/model2.ckpt"); + Console.WriteLine($"Model saved in path: {save_path}"); } } } diff --git a/test/TensorFlowNET.UnitTest/ManagedAPI/ControlFlowApiTest.cs b/test/TensorFlowNET.UnitTest/ManagedAPI/ControlFlowApiTest.cs index 8366c070..6d7182e0 100644 --- a/test/TensorFlowNET.UnitTest/ManagedAPI/ControlFlowApiTest.cs +++ b/test/TensorFlowNET.UnitTest/ManagedAPI/ControlFlowApiTest.cs @@ -57,12 +57,10 @@ namespace TensorFlowNET.UnitTest.ManagedAPI var input = tf.placeholder(TF_DataType.TF_FLOAT, new Shape(6)); var scan = tf.scan(fn, input); - using (var sess = tf.Session()) - { - sess.run(tf.global_variables_initializer()); - var result = sess.run(scan, new FeedItem(input, np.array(1, 2, 3, 4, 5, 6))); - Assert.AreEqual(new float[] { 1, 3, 6, 10, 15, 21 }, result.ToArray()); - } + var sess = tf.Session(); + sess.run(tf.global_variables_initializer()); + var result = sess.run(scan, new FeedItem(input, np.array(1, 2, 3, 4, 5, 6))); + Assert.AreEqual(new float[] { 1, 3, 6, 10, 15, 21 }, result.ToArray()); } } } diff --git a/test/TensorFlowNET.UnitTest/PythonTest.cs b/test/TensorFlowNET.UnitTest/PythonTest.cs index 0ee8762c..50cc2b32 100644 --- a/test/TensorFlowNET.UnitTest/PythonTest.cs +++ b/test/TensorFlowNET.UnitTest/PythonTest.cs @@ -196,23 +196,21 @@ namespace TensorFlowNET.UnitTest // return self._eval_helper(tensors) // else: { - using (var sess = tf.Session()) + var sess = tf.Session(); + var ndarray = tensor.eval(sess); + if (typeof(T) == typeof(double)) { - var ndarray = tensor.eval(sess); - if (typeof(T) == typeof(double)) - { - double x = ndarray; - result = x; - } - else if (typeof(T) == typeof(int)) - { - int x = ndarray; - result = x; - } - else - { - result = ndarray; - } + double x = ndarray; + result = x; + } + else if (typeof(T) == typeof(int)) + { + int x = ndarray; + result = x; + } + else + { + result = ndarray; } return (T)result; diff --git a/test/TensorFlowNET.UnitTest/StatusTest.cs b/test/TensorFlowNET.UnitTest/StatusTest.cs index 5106cb6f..6dcdc158 100644 --- a/test/TensorFlowNET.UnitTest/StatusTest.cs +++ b/test/TensorFlowNET.UnitTest/StatusTest.cs @@ -28,7 +28,6 @@ namespace TensorFlowNET.UnitTest.Basics public void DeleteStatus() { var s = new Status(); - s.Dispose(); } } }